Model Selection and Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a55a3a70-d774-4b79-922a-5dd06b4a11f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection and Evaluation !!\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Selection and Evaluation !!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e66dc92-1e3e-4c6e-a9a7-1e20e45e573b",
   "metadata": {},
   "source": [
    "### 1. Cross Validation\n",
    "- Simple models underfit.\n",
    "- Accuracy for training data & validation data is not much different.\n",
    "- But, accurcy ain't that great.\n",
    "- This situation is of low variance & high bias\n",
    "- On moving towards complex models, accuracy improves.\n",
    "- But, gap between accuracy on training data & validation data increases\n",
    "- This situation is of high variance & low bias\n",
    "- We need to compare across models to find the best model.\n",
    "- We need to compare across all hyper-parameters for a particular model.\n",
    "- The data that is used for training should not be used for validation.\n",
    "- The validation accuracy is the one that we claims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "17e1945a-5236-48e3-8466-1ebf6426f019",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b4acc967-f311-4c46-ba9d-13598559223f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9b40b92d-bf1d-4c89-8a22-58c840e25880",
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0899ae6-2efe-40b1-a5da-20c2dccf3699",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a89bfd44-7343-4416-88af-13110b7e222e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(digits.images[0],cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d94fa003-7ca2-4004-a412-1b783682e15d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3a6169e5-f304-4cd9-a354-f4ea604752f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "659f8163-a62f-411e-898c-81d686e435de",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(digits.data, digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b1ad8aa3-9dc0-44d9-8066-81a3127bfff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=10)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ecd53c46-6a89-484c-88e7-0fd1a0b2175a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8511111111111112"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a2546c7-e455-4751-835f-1c1a459cb45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9821826280623608"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(trainX,trainY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ce3ee6-7436-414a-ba93-73ce419d7cb3",
   "metadata": {},
   "source": [
    "#### - Decreasing the complexity of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2bfca6b-47de-4338-a636-cec2698aef42",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6573adc5-ead8-4ecc-aef7-684e81549d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=7)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=7)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3942c7e8-e478-4be4-9439-c49d88208e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "430719c6-b196-45ae-8685-47e5b4457df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8856718634001485"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(trainX,trainY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae6ffbc-cc57-445c-820a-b49b9fd50cbb",
   "metadata": {},
   "source": [
    "##### Observation : With decrease in complexity the gap in training & validation accuracy also decreased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b0fadd-f596-4232-8968-02aaba092cf3",
   "metadata": {},
   "source": [
    "#### Cross Validation API\n",
    "- Splits data into k parts.\n",
    "- Use k - 1 parts for training the model\n",
    "- Use kth part for validation\n",
    "- Repeat the above steps multiple times to get a genralized behaviour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6d7b75fd-1191-4a7b-b241-8ecfed2ddb77",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fbd17730-e332-4c2d-a978-187174af0519",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(dt, digits.data, digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e8dd9852-7850-4ad9-babf-a9ed00c0f2e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7418368926029093"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94c0574-e770-4937-8326-0dfabd5976e4",
   "metadata": {},
   "source": [
    "#### Cross-validate Function : Scores for multiple matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d6266a37-84a8-49cd-8fcc-61179d7ac86f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "468e658f-1d75-4a9a-b8b9-058e38e06d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = ['precision_macro', 'recall_macro', 'accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e8f03d53-f7b2-4600-9941-245e5f6a5562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0.01998878, 0.01600456, 0.01900125, 0.01799965, 0.0170002 ]),\n",
       " 'score_time': array([0.14222765, 0.00799966, 0.00699782, 0.00900006, 0.00700092]),\n",
       " 'test_precision_macro': array([0.76669623, 0.70161034, 0.78050181, 0.78913064, 0.77884105]),\n",
       " 'test_recall_macro': array([0.758432  , 0.65594595, 0.77035544, 0.77023166, 0.76279065]),\n",
       " 'test_accuracy': array([0.75833333, 0.65555556, 0.77158774, 0.77158774, 0.76044568])}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(dt, digits.data, digits.target, scoring=scoring, cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f6a9d8-8544-439b-875c-ae43de33c6ff",
   "metadata": {},
   "source": [
    "\n",
    "#### Stratification for dealing with imbalanced Classes\n",
    "- StratifiedKFold\n",
    "- Class frequencies are preserved in data splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "877ff470-e92c-4aae-9f2d-7e62ac8cc1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "83bfe97a-8d6f-4f86-86c8-7aaf2459b5f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.append(np.ones(12),np.zeros(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6d9b1982-d0e3-4411-b211-67e0ffbd72bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.ones((18,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3db90e90-b930-4899-a072-b5f00170c2ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3fbf3a6e-d07b-4be5-a382-328c22f1a0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b06e16df-b8c4-4cc9-aacb-94859d159d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(array([ 4,  5,  6,  7,  8,  9, 10, 11, 14, 15, 16, 17]),\n",
       "  array([ 0,  1,  2,  3, 12, 13])),\n",
       " (array([ 0,  1,  2,  3,  8,  9, 10, 11, 12, 13, 16, 17]),\n",
       "  array([ 4,  5,  6,  7, 14, 15])),\n",
       " (array([ 0,  1,  2,  3,  4,  5,  6,  7, 12, 13, 14, 15]),\n",
       "  array([ 8,  9, 10, 11, 16, 17]))]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(skf.split(X,Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7d5ce6d8-9eb2-4dda-a6b6-81ddbbef2483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[[ 4,  5,  6,  7,  8,  9, 10, 11, 14, 15, 16, 17]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7486cf90-8b67-4d4f-9aa1-89bdb4660519",
   "metadata": {},
   "source": [
    "\n",
    "### 2. Hyperparameter Tuning\n",
    "- Model parameters are learnt by learning algorithms based on data\n",
    "- Hyper-parameters needs to be configured\n",
    "- Hyper-parameters are data dependent & many times need experiments to find the best\n",
    "- sklearn provides GridSerach for finding the best hyper-parameters\n",
    "#### Exhaustive GridSearch\n",
    "- Searches sequentially for all the configued params\n",
    "- For all possible combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f17c666f-a00a-44d5-aff4-79fdc61d5eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(digits.data, digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c0274d47-92dd-432c-af04-440979dba423",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "81ff7be3-37bf-4114-89e4-df0d703953f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "596607e7-f384-41db-a05a-aa4a4213a81c",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(dt, param_grid={'max_depth':range(5,30,5)}, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3d7331ff-0e8d-4a7e-93ea-be2b3a97c197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: range(5, 30, 5)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: range(5, 30, 5)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'max_depth': range(5, 30, 5)})"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(digits.data,digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2ae3081c-6530-4a70-ab01-2ef3497a1d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 20}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "594b8784-93ae-4d3e-b249-2fcbfd4eba88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.789124110182606"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "600759c5-cbe1-4e43-adf0-d5875369e601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=20)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f8b8d4-8261-4184-8e47-def9e7beb54e",
   "metadata": {},
   "source": [
    "#### RandomizedSearch\n",
    "- Unlike GridSearch, not all parameters are tried & tested\n",
    "- But rather a fixed number of parameter settings is sampled from the specified distributions.\n",
    "- Comparing GridSearch and RandomSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "44cebb61-bc77-4013-8c13-0cc2e0aa4531",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from time import time\n",
    "\n",
    "#randint is an intertor for generating numbers between range specified\n",
    "from scipy.stats import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bcfe1737-b821-4a98-b5d6-dc02e9674822",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = digits.data\n",
    "Y = digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ecc466d6-57c0-485e-9885-f3497d206c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2fa7b837-1cec-4922-a83e-df1b1569db77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify parameters and distributions to sample from\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"max_features\": randint(1,11),\n",
    "              \"min_samples_split\": randint(2, 11),\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d46126f5-5682-4802-bcda-cc3fa94e4d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': [3, None],\n",
       " 'max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen at 0x1fd908254c0>,\n",
       " 'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen at 0x1fd90688e60>,\n",
       " 'bootstrap': [True, False],\n",
       " 'criterion': ['gini', 'entropy']}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cc5c4365-5adb-45fc-94ad-966282a5d59b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7f83896b-16b7-44c5-b281-7c2ea21ee9d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV took 9.43 seconds for 20 candidates parameter settings.\n"
     ]
    }
   ],
   "source": [
    "n_iter_search = 20\n",
    "random_search = RandomizedSearchCV(rf, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search, cv=5)\n",
    "\n",
    "start = time()\n",
    "random_search.fit(X, Y)\n",
    "print(\"RandomizedSearchCV took %.2f seconds for %d candidates\"\n",
    "      \" parameter settings.\" % ((time() - start), n_iter_search))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1a7f0275-2137-4343-851c-aa839d752354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9321231816774993"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "980e1aca-4a61-459b-aabc-c9b836b6b64c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 28.52 seconds for 72 candidate parameter settings.\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\"max_depth\": [3, None],\n",
    "              \"max_features\": [1, 3, 10],\n",
    "              \"min_samples_split\": [2, 3, 10],\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "# run grid search\n",
    "grid_search = GridSearchCV(rf, param_grid=param_grid, cv=5)\n",
    "start = time()\n",
    "grid_search.fit(X, Y)\n",
    "\n",
    "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n",
    "      % (time() - start, len(grid_search.cv_results_['params'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d74eb5de-fa5f-4503-9ed5-c71bbf250f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9343546889507893"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216b5e77-f7cf-4efb-9933-7bbe039d47a7",
   "metadata": {},
   "source": [
    "- GridSearch & RandomizedSearch can fine tune hyper-parameters of transformers as well when part of pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de083b9-c64d-4b54-ba90-a1a3ba843cc3",
   "metadata": {},
   "source": [
    "\n",
    "### 3. Model Evaluation\n",
    "-- Three different ways to evaluate quality of model prediction\n",
    "- Score method of estimators, a default method is configured .i.e r2_score for regression, accuracy for classification\n",
    "- Model evalutaion tools like cross_validate or cross_val_score also returns accuracy\n",
    "- Metrices module is rich with various prediction error calculation techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b110ad50-da0f-4da9-bf1c-7fd2a4917ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "09a9a15c-1dd9-414f-9dbe-8023ba4b1798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=20)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=20)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(trainX, trainY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63950537-392f-4664-add9-9ab2e0a79250",
   "metadata": {},
   "source": [
    "- Technique 1 - Using score function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "946cf45e-af33-4df9-88af-a4ae0fbb2f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9644444444444444"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(testX,testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d1a93a-5103-4870-b13b-dd9ee259282a",
   "metadata": {},
   "source": [
    "- Technique 2 - Using cross_val_score as discussed above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "41176185-a3cc-4177-979d-edcb457c435b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90555556, 0.90555556, 0.94986072, 0.95264624, 0.91364903])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(rf,X,Y,cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8640e119-a1a2-46e9-a002-4ddc8a96187f",
   "metadata": {},
   "source": [
    "#### Cancer prediction sample for understanding metrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "89efde95-08d2-4c9b-bb72-a3345f209d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6ce13209-280c-48ba-bbc5-1ae5dad18397",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9be954ba-5968-499d-ab4d-c1cc98a2c3a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_data = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b1e175db-03e6-4055-bc81-cda44aca89e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(cancer_data.data, cancer_data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ac493fdc-3f3c-4844-8b5d-3541ae35f42e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3d8b866c-6944-41d0-b5c1-258df603f6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = dt.predict(testX)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d20f75-4011-4b06-ab7f-015e7b7f9452",
   "metadata": {},
   "source": [
    "\n",
    "#### Technique 3 - Using metrices\n",
    "- Classfication metrices\n",
    "- Accuracy Score - Correct classification vs ( Correct classification + Incorrect Classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a9cde278-6424-4d16-9603-81c91e55ab4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f34c84a7-39bd-4df1-961d-0cdb276dcf23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9370629370629371"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_pred=pred, y_true=testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9fa1404-3403-4e69-b1f5-fd7071b65092",
   "metadata": {},
   "source": [
    "-- Confusion Matrix - Shows details of classification inclusing TP,FP,TN,FN\n",
    "- True Positive (TP), Actual class is 1 & prediction is also 1\n",
    "- True Negative (TN), Actual class is 0 & prediction is also 0\n",
    "- False Positive (FP), Acutal class is 0 & prediction is 1\n",
    "- False Negative (FN), Actual class is 1 & prediction is 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "90c9319c-8135-4b8f-ad39-b20bf6bb1caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  3],\n",
       "       [ 6, 84]], dtype=int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_pred=pred, y_true=testY, labels=[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1403082-b4b3-49a4-80f7-79bfbfe63f29",
   "metadata": {},
   "source": [
    "-- Precision Score\n",
    "- Ability of a classifier not to label positive if the sample is negative\n",
    "- Claculated as TP/(TP+FP)\n",
    "- We don't want a non-spam mail to be marked as spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "366a1eee-8405-4c20-a929-e3fca0fccea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9655172413793104"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(y_pred=pred, y_true=testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "596c3592-254c-43b2-8a67-0c4bc1ce7f1f",
   "metadata": {},
   "source": [
    "-- Recall Score\n",
    "- Ability of classifier to find all positive samples\n",
    "- It's ok to predict patient tumor to be cancer so that it undergoes more test\n",
    "- But it is not ok to miss a cancer patient without further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0cd8f39c-489f-44c1-b521-9f8ea827ef5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(y_pred=pred, y_true=testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b83c31f-6e3b-4f4f-a0b4-35a2d9d6370c",
   "metadata": {},
   "source": [
    "-- F1 score\n",
    "- Weighted average of precision & recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "97e1dc4d-7a72-4d88-ada6-9e29b3f73a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9491525423728814"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(y_pred=pred, y_true=testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "746ec355-b861-4336-b106-2a1dae6974c2",
   "metadata": {},
   "source": [
    "#### ROC & AUC\n",
    "\n",
    "- House Price Prediction - Understanding matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5d2e768c-628a-4279-8521-d1e753923c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d68851ac-ba4f-4f6c-81af-3eeebaea7e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#house_data = california_housing.fetch_california_housing()\n",
    "\n",
    "house_data = fetch_california_housing(as_frame=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4a003a18-6f03-4649-8798-aa25ab6f2a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3c3a1ab5-e61e-44d9-8ca9-f3c0eadd5cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e3922f0d-2f0d-4e49-919c-72f8c20013c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(house_data.data, house_data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5f803ba6-2cc2-464b-a317-a5574bc44afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lr.predict(house_data.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea00cf6c-1c3e-40c2-a29c-35700e3c379d",
   "metadata": {},
   "source": [
    "#### Matrices for Regression\n",
    "-- mean squared error\n",
    "- Sum of squares of difference between expected value & actual value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "70ff02b1-d3d4-42cf-aa73-a04259eababa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5243209861846071"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_pred=pred, y_true=house_data.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ba9e7a-a642-4bd1-9e6a-8da4479ac18c",
   "metadata": {},
   "source": [
    "-- mean absolute error\n",
    "- Sum of abs of difference between expected value & actual valu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c945b795-481e-4af4-8b99-827cf8518f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5311643817546474"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_pred=pred, y_true=house_data.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b725b28c-09fb-4fcc-a5ad-d80367cf7ed7",
   "metadata": {},
   "source": [
    "-- r2 score\n",
    "- Returns accuracy of model in the scale of 0 & 1\n",
    "- It measures goodness of fit for regression models\n",
    "- Calculated as = (variance explained by the model)/(Total variance)\n",
    "- High r2 means target is close to prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ac80fac7-5b2c-4518-bb27-8e5a31ccccc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6062326851998051"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_pred=pred, y_true=house_data.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee075a79-e6a8-4070-93f1-77c8244bbabf",
   "metadata": {},
   "source": [
    "##### Metrices for Clustering\n",
    "- Two forms of evaluation\n",
    "- supervised, which uses a ground truth class values for each sample\n",
    "i) completeness_score\n",
    "ii) homogeneity_score\n",
    "- unsupervised, which measures the quality of model itself\n",
    "i) silhoutte_score\n",
    "ii) calinski_harabaz_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efc53b0-ec7e-4b2e-b29a-2367c862ecfd",
   "metadata": {},
   "source": [
    "\n",
    "#### completeness_score\n",
    "- A clustering result satisfies completeness if all the data points that are members of a given class are elements of the same cluster.\n",
    "- Accuracy is 1.0 if data belonging to same class belongs to same cluster, even if multiple classes belongs to same cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "eeac9765-35ca-41a6-b26a-30a84d9a9864",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.cluster import completeness_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1bfd7a98-da74-420c-9d7b-804cb84ddffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completeness_score( labels_true=[10,10,11,11],labels_pred=[1,1,0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fec3737-b11c-48e0-8b0d-f99f89e88c3a",
   "metadata": {},
   "source": [
    "- The acuracy is 1.0 because all the data belonging to same class belongs to same cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ad5526bb-2cb0-46d7-a575-57d6f58e991b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3836885465963443"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completeness_score( labels_true=[11,22,22,11],labels_pred=[1,0,1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b690669-668d-446b-a599-79e171457f70",
   "metadata": {},
   "source": [
    "- The accuracy is .3 because class 1 - [11,22,11], class 2 - [22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ffbb7da5-a1cc-4414-962f-6da9652b114d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(completeness_score([10, 10, 11, 11], [0, 0, 0, 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb6d303-de9b-4d5c-9b80-365aaa7d7254",
   "metadata": {},
   "source": [
    "#### homogeneity_score\n",
    "- A clustering result satisfies homogeneity if all of its clusters contain only data points which are members of a single class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f2b966a6-1d93-4cfd-bc73-d91793d5ad9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.cluster import homogeneity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "32c07e75-3d49-4896-8770-31c76b5185ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "594ed768-72a2-4b92-b04b-7c85621c8a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999999"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ad1f02c4-b90c-4ad3-a757-fa585fcec54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homogeneity_score([0, 0, 0, 0], [1, 1, 0, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b51d5fb-5af6-457e-b44f-5f74ab1ff0b1",
   "metadata": {},
   "source": [
    "- Same class data is broken into two clusters\n",
    "\n",
    "#### silhoutte_score\n",
    "- The Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample.\n",
    "- The Silhouette Coefficient for a sample is (b - a) / max(a, b). To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of.\n",
    "- Selecting the number of clusters with silhouette analysis on KMeans clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b05dbf6a-cdcc-4d42-bd4a-b227914f603a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "X, Y = make_blobs(n_samples=500,\n",
    "                  n_features=2,\n",
    "                  centers=4,\n",
    "                  cluster_std=1,\n",
    "                  center_box=(-10.0, 10.0),\n",
    "                  shuffle=True,\n",
    "                  random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5504fc0e-4a2d-466b-a911-2888027e762d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],s=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "95306874-8789-499e-abca-1b45359b5a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "range_n_clusters = [2, 3, 4, 5, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "49231801-10f8-4fef-861f-8c1c2b5b40a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f6f3cb6d-70bc-496f-bef3-259be9782456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 0.7049787496083261\n",
      "3 0.5882004012129721\n",
      "4 0.6505186632729437\n",
      "5 0.5433481058914456\n",
      "6 0.4427616022488244\n"
     ]
    }
   ],
   "source": [
    "for n_cluster in range_n_clusters:\n",
    "    kmeans = KMeans(n_clusters=n_cluster)\n",
    "    kmeans.fit(X)\n",
    "    labels = kmeans.predict(X)\n",
    "    print (n_cluster, silhouette_score(X,labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b519aef2-59b7-4ad3-963f-7c3cb441b81a",
   "metadata": {},
   "source": [
    "- The best number of clusters is 2\n",
    "#### calinski_harabaz_score\n",
    "- The score is defined as ratio between the within-cluster dispersion and the between-cluster dispersion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "5aea8be4-002f-4954-85fa-51716e0212e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 1604.112286409658\n",
      "3 1809.991966958033\n",
      "4 2704.4858735121097\n",
      "5 2202.0290421721215\n",
      "6 1961.0783160103947\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#from sklearn.metrics import calinski_harabaz_score wrong\n",
    "from sklearn.metrics import calinski_harabasz_score\n",
    "\n",
    "for n_cluster in range_n_clusters:\n",
    "    kmeans = KMeans(n_clusters=n_cluster)\n",
    "    kmeans.fit(X)\n",
    "    labels = kmeans.predict(X)\n",
    "    print (n_cluster, calinski_harabasz_score(X,labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f91babe-cabf-41b4-8912-3af9bed3e20f",
   "metadata": {},
   "source": [
    "\n",
    "### 4. Model Persistance\n",
    "- Model training is an expensive process\n",
    "- It is desireable to save the model for future reuse\n",
    "- using pickle & joblib this can be achieved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "2b4e80f9-94cd-418d-87d3-a7eddb7b41c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "736c223a-d09a-4440-b560-8cb01ce7bff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pickle.dumps(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "56d61cb7-59bc-4ad8-be6b-511c17581e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-10 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle.loads(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "1c9485f7-9792-4c31-a423-cb9c81d152f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bytes"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe16f860-8c01-42f8-9085-63828d81dae3",
   "metadata": {},
   "source": [
    "\n",
    "- joblib is better extension of pickle\n",
    "- Doesn't convert into string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "a7570f81-3745-4ed8-b62d-4f5abe5f5527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: joblib in c:\\users\\pc\\anaconda3\\lib\\site-packages (1.4.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install joblib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "25a355b2-d311-4915-820a-87dba3082d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.externals import joblib\n",
    "\n",
    "import joblib\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d6f7d53a-69f6-4914-bf60-5ecd7aebe2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dt.joblib']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(dt, 'dt.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3239c1a9-cb0e-4512-84f3-fcb57dcab559",
   "metadata": {},
   "source": [
    "- Loading the file back into model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5ceda310-f405-4537-b6bc-86df8d5ab824",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = joblib.load('dt.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "361947f4-718c-4c7f-95a8-77c4da7026af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-11 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-11 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-11 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-11 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d32b843-1e4c-46b3-acb8-d64d93e1b452",
   "metadata": {},
   "source": [
    "\n",
    "### 5. Validation Curves\n",
    "- To validate a model, we need a scoring function.\n",
    "- Create a grid of possible hyper-prameter configuration.\n",
    "- Select the hyper-parameter which gives the best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "350e1b52-d275-4bc9-9f8c-23b3dec07946",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "param_range = np.arange(1, 50, 2)\n",
    "\n",
    "train_scores, test_scores = validation_curve(RandomForestClassifier(), \n",
    "                                             digits.data, \n",
    "                                             digits.target, \n",
    "                                             param_name=\"n_estimators\", \n",
    "                                             param_range=param_range,\n",
    "                                             cv=3, \n",
    "                                             scoring=\"accuracy\", \n",
    "                                             n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "94e022d4-da3a-4542-b887-856c099ae12f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "plt.plot(param_range, train_mean, label=\"Training score\", color=\"black\")\n",
    "plt.plot(param_range, test_mean, label=\"Cross-validation score\", color=\"dimgrey\")\n",
    "\n",
    "plt.title(\"Validation Curve With Random Forest\")\n",
    "plt.xlabel(\"Number Of Trees\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.tight_layout()\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28bf5d1-0b75-4ecf-ad2a-86298ff1dec1",
   "metadata": {},
   "source": [
    "### 6. Learning Curves\n",
    "- Learning curves shows variation in training & validation score on increasing the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3f3998ed-779f-4ef4-a5c4-b9e9bca7c39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "07f6c739-1d79-423c-afa1-0d91d543f401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Generate a synthetic dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n",
    "\n",
    "# Define the model\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Create learning curves\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    model, X, y, cv=5, scoring='accuracy', train_sizes=np.linspace(0.1, 1.0, 10), random_state=42\n",
    ")\n",
    "\n",
    "# Calculate mean and standard deviation of train and test scores\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot learning curves\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.grid()\n",
    "\n",
    "# Training scores\n",
    "plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
    "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
    "\n",
    "# Validation scores\n",
    "plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
    "\n",
    "# Labels and title\n",
    "plt.title(\"Learning Curve\")\n",
    "plt.xlabel(\"Training Examples\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24fea3db-4ff7-4f3d-a05a-740420edf679",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}