Classification-practise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51f7b97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f05b649",
   "metadata": {},
   "outputs": [],
   "source": [
    "from packaging import version\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a18609cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f11dbc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2aa95245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', as_frame=False)\n",
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aeb7b904",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = mnist['data'], mnist['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "119ffc4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 784)\n",
      "(70000,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1701da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_digit = X[0]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap=mpl.cm.binary)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14414313",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eafa9b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8c8fa78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28,28)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary,\n",
    "              interpolation = \"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9a5c224",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e839f6b9",
   "metadata": {},
   "source": [
    "# Simple Binary Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d07a0ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train ==5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff309569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eecbb25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a110b6ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95035, 0.96035, 0.9604 ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring = \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddcf0070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9669\n",
      "0.91625\n",
      "0.96785\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
    "\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds =  X_train[train_index]\n",
    "    y_train_folds = y_train_5[train_index]\n",
    "    X_test_fold = X_train[test_index]\n",
    "    y_test_fold = y_train_5[test_index]\n",
    "    \n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct/ len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eaa33b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X), 1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb32c2e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91125, 0.90855, 0.90915])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "never_5_clf = Never5Classifier()\n",
    "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "712647ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a72f571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac767b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_perfect_predictions = y_train_5\n",
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c737892d",
   "metadata": {},
   "source": [
    "# Precision and Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b19b2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "precision_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3b98404d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "cm[1,1]/(cm[0,1]+cm[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e3b1ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e64ac1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[1,1]/(cm[1,0]+cm[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9529d103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343846"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "de3fe645",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2164.22030239]\n"
     ]
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "print(y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8dfdcd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores>threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2d909b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4593ca00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 8000\n",
    "y_some_digit_pred = (y_scores>threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ca48b0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
    "                            method='decision_function')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3208e256",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2ed79134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.legend(loc=\"center right\", fontsize=16) \n",
    "    plt.xlabel(\"Threshold\", fontsize=16)        \n",
    "    plt.grid(True)                              \n",
    "    plt.axis([-50000, 50000, 0, 1])             \n",
    "\n",
    "\n",
    "\n",
    "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 4))                                                                  # Not shown\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], \"r:\")                 # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [0.9, 0.9], \"r:\")                                # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], \"r:\")# Not shown\n",
    "plt.plot([threshold_90_precision], [0.9], \"ro\")                                             # Not shown\n",
    "plt.plot([threshold_90_precision], [recall_90_precision], \"ro\")                             # Not shown                                            # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "07f70315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6ac37068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "plt.plot([recall_90_precision, recall_90_precision], [0., 0.9], \"r:\")\n",
    "plt.plot([0.0, recall_90_precision], [0.9, 0.9], \"r:\")\n",
    "plt.plot([recall_90_precision], [0.9], \"ro\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a8da9ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "32e5c390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3370.0194991439557"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "887bb234",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_90_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3649651e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9000345901072293"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c8a5d6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4799852425751706"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7054d0cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, threshold = roc_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "34e554cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(fpr, tpr, label = None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0,1],[0,1], 'k--')\n",
    "    plt.axis([0,1,0,1])\n",
    "    plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate (Recall)', fontsize=16)\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bdb733ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "fpr_90 = fpr[np.argmax(tpr>=recall_90_precision)]\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")\n",
    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")\n",
    "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3c28b89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9604938554008616"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cdcc9680",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                   method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4948c581",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:,-1]\n",
    "fpr_forest, tpr_forest, threshold_forest = roc_curve(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b8ba41a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recall_for_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")\n",
    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")\n",
    "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_for_forest], \"r:\")\n",
    "plt.plot([fpr_90], [recall_for_forest], \"ro\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "db789fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983436731328145"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ac8c14a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905083315756169"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n",
    "precision_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "93969410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8662608374838591"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5378e2",
   "metadata": {},
   "source": [
    "# Multiclass Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1c3c2c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma=\"auto\", random_state = 42)\n",
    "svm_clf.fit(X_train[:1000], y_train[:1000])\n",
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "21de5724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.81585438,  7.09167958,  3.82972099,  0.79365551,  5.8885703 ,\n",
       "         9.29718395,  1.79862509,  8.10392157, -0.228207  ,  4.83753243]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f8b7176f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f72d4ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eaae6e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a299abbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\n",
    "ovr_clf.fit(X_train[:1000], y_train[:1000])\n",
    "ovr_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1232ca88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovr_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "418cb12a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3], dtype=uint8)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "986d4dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n",
       "          1823.73154031, -22320.14822878,  -1385.80478895,\n",
       "        -26188.91070951, -16147.51323997,  -4604.35491274,\n",
       "        -12050.767298  ]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2093b5f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87365, 0.85835, 0.8689 ])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "40dd904e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87365, 0.85835, 0.8689 ])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b70a7b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8983, 0.891 , 0.9018])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a80c71",
   "metadata": {},
   "source": [
    "# Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a6404f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c7d416f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5577,    0,   22,    5,    8,   43,   36,    6,  225,    1],\n",
       "       [   0, 6400,   37,   24,    4,   44,    4,    7,  212,   10],\n",
       "       [  27,   27, 5220,   92,   73,   27,   67,   36,  378,   11],\n",
       "       [  22,   17,  117, 5227,    2,  203,   27,   40,  403,   73],\n",
       "       [  12,   14,   41,    9, 5182,   12,   34,   27,  347,  164],\n",
       "       [  27,   15,   30,  168,   53, 4444,   75,   14,  535,   60],\n",
       "       [  30,   15,   42,    3,   44,   97, 5552,    3,  131,    1],\n",
       "       [  21,   10,   51,   30,   49,   12,    3, 5684,  195,  210],\n",
       "       [  17,   63,   48,   86,    3,  126,   25,   10, 5429,   44],\n",
       "       [  25,   18,   30,   64,  118,   36,    1,  179,  371, 5107]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "13614035",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confussion_matrix(matrix):\n",
    "    fig = plt.figure(fig=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(matrix)\n",
    "    fig.colourbar(cax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "279f1541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1210544a",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims = True)\n",
    "norm_conf_mx = conf_mx/row_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e43ebbc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ee8f49",
   "metadata": {},
   "source": [
    "# Multilabel Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1bef7e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train %2 ==1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "46579454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b32c73",
   "metadata": {},
   "source": [
    "# Multioutput Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8c6cbef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise\n",
    "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "387a2e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[0]])\n",
    "plot_digit(clean_digit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e71f5bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=4, weights=&#x27;distance&#x27;)</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 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 sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=4, weights=&#x27;distance&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4, weights='distance')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier(weights='distance', n_neighbors=4)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9b8a076",
   "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": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}