decision_boundary_multiclass_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sn\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading IRIS Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris = pd.DataFrame(iris['data'],columns = iris['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris['target'] = iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_model = LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_iris.iloc[:,[0,1]]\n",
    "y = df_iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6.69601283,  1.23937402, -7.93538685]),\n",
       " array([[-2.36181485,  2.07689467],\n",
       "        [ 0.60836042, -1.43469174],\n",
       "        [ 1.75345443, -0.64220294]]))"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_model.intercept_, iris_model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Decision Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.69601283  1.23937402 -7.93538685]\n",
      "[[-2.36181485  2.07689467]\n",
      " [ 0.60836042 -1.43469174]\n",
      " [ 1.75345443 -0.64220294]]\n"
     ]
    }
   ],
   "source": [
    "print(iris_model.intercept_) \n",
    "print(iris_model.coef_)\n",
    "\n",
    "weights0 = [iris_model.intercept_[0],iris_model.coef_[0,0],iris_model.coef_[0,1]]\n",
    "weights1 = [iris_model.intercept_[1],iris_model.coef_[1,0],iris_model.coef_[1,1]]\n",
    "weights2 = [iris_model.intercept_[2],iris_model.coef_[2,0],iris_model.coef_[2,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines._AxLine at 0x228886df520>"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g =sn.scatterplot(x=\"sepal length (cm)\",y=\"sepal width (cm)\",\n",
    "              hue=\"target\",\n",
    "              data=df_iris,palette=['green','red','blue']);\n",
    "\n",
    "x1 = (-weights0[0] - weights0[2] * df_iris.iloc[0,1])/weights0[1]\n",
    "x2 = (-weights0[0] - weights0[2] * df_iris.iloc[4,1])/weights0[1]\n",
    "\n",
    "plt.axline((x1, df_iris.iloc[0,1]), (x2, df_iris.iloc[4,1]), color = \"green\")\n",
    "\n",
    "x1 = (-weights1[0] - weights1[2] * df_iris.iloc[0,1])/weights1[1]\n",
    "x2 = (-weights1[0] - weights1[2] * df_iris.iloc[4,1])/weights1[1]\n",
    "\n",
    "plt.axline((x1, df_iris.iloc[0,1]), (x2, df_iris.iloc[4,1]), color = \"red\")\n",
    "\n",
    "x1 = (-weights2[0] - weights2[2] * df_iris.iloc[0,1])/weights2[1]\n",
    "x2 = (-weights2[0] - weights2[2] * df_iris.iloc[4,1])/weights2[1]\n",
    "\n",
    "plt.axline((x1, df_iris.iloc[0,1]), (x2, df_iris.iloc[4,1]), color = \"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}