w3-logistic-regression-scikitlearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f198972",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])\n",
    "y = np.array([0, 0, 0, 1, 1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddca880b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = LogisticRegression()\n",
    "m.fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a14f3f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0],X[y==0,1],marker='x')\n",
    "plt.scatter(X[y==1,0],X[y==1,1],marker='o')\n",
    "plt.scatter(X[m.predict(X)>=0,0],X[m.predict(X)>=0,1],marker='+')\n",
    "plt.show()\n",
    "\n",
    "print(m.score(X,y))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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