supervised.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "inyeki tozihatesh kamel nist ke badan kamel mikonam \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.target = df.target.map(dict(zip(range(3), iris.target_names)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "#voroodi ha va khorooji ha ra define mikonim\n",
    "x = df[[\"sepal length (cm)\", \"petal length (cm)\",\n",
    "       \"petal width (cm)\"]]\n",
    "y = df.target\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "                                    x, y, test_size=0.3, stratify=y)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbors = np.arange(1, 30)\n",
    "train_accuracy = np.empty(len(neighbors))\n",
    "test_accuracy = np.empty(len(neighbors))\n",
    "for i, k in enumerate(neighbors):  \n",
    "    KNN = KNeighborsClassifier(n_neighbors=k, )\n",
    "    KNN.fit(x_train, y_train)\n",
    "    train_accuracy[i] = KNN.score(x_train, y_train)\n",
    "    test_accuracy[i] = KNN.score(x_test, y_test)\n",
    "    \n",
    "plt.plot(neighbors, train_accuracy, label=\"train acuuracy\", marker='+', mew=4, ls='--', lw=2)\n",
    "plt.plot(neighbors, test_accuracy, label=\"test acuuracy\", marker='o', mew=1)\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.xlabel(\"Neighbor number\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[[\"sepal length (cm)\", \"petal length (cm)\",\n",
    "       \"petal width (cm)\"]]\n",
    "y = df.target\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "                                    x, y, test_size=0.3, stratify=y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTC = DecisionTreeClassifier()\n",
    "DTC.fit(x_train, y_train)\n",
    "DTC.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression\n",
    "\n",
    "استفاده از OLS برای اندازه گیری فواصل بین داده ها برای به دست اوردن تابع هزینه (ERROR FUNCTION)\n",
    "\\begin{aligned}\n",
    "y &= \\alpha_{1} \\cdot x_{1} + \\alpha_{2} \\cdot x_{2} + b\n",
    "\\end{aligned}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[[\"sepal length (cm)\", \"petal length (cm)\",\n",
    "       \"petal width (cm)\"]]\n",
    "y = df.target\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "                                    x, y, test_size=0.3, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9341417685488267"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression()\n",
    "LR.fit(x_train, y_train)\n",
    "LR.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  Price  \n",
       "0       15.3  396.90   4.98   24.0  \n",
       "1       17.8  396.90   9.14   21.6  \n",
       "2       17.8  392.83   4.03   34.7  \n",
       "3       18.7  394.63   2.94   33.4  \n",
       "4       18.7  396.90   5.33   36.2  \n",
       "..       ...     ...    ...    ...  \n",
       "501     21.0  391.99   9.67   22.4  \n",
       "502     21.0  396.90   9.08   20.6  \n",
       "503     21.0  396.90   5.64   23.9  \n",
       "504     21.0  393.45   6.48   22.0  \n",
       "505     21.0  396.90   7.88   11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston = load_boston()\n",
    "df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "df['Price'] = boston.target\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "اگر از تمام فیچر های این دیتاست استفاده شود با توجه به نداشتن کوریلیشن تمامی داده ها خطا بسیار زیاد میشود\n",
    "\n",
    "برای چک کردن میزان خطای به وجود آمده از MSE (Mean Square Error) استفاده میشود\n",
    "\n",
    "\n",
    "میزان نزدیک بودن عدد پردکیت شده نسبت به عدد واقعی را مقایسه میکند\n",
    "\n",
    "عملیات پایین با خطای زیاد انجام شده است\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "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"
    }
   ],
   "source": [
    "x = boston.data\n",
    "y = boston.target\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "                                    x, y, test_size=0.3)\n",
    "LR = LinearRegression()\n",
    "LR.fit(x_train, y_train)\n",
    "y_pred = LR.predict(x_test)\n",
    "plt.scatter(y_test, y_pred)\n",
    "\n",
    "plt.xlabel(\"Real Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.33316927631535"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find MSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "mse\n",
    "# agar jazre MSE be 0 nazdik bashe error e kamtari darim\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "برای بهبود عملکرد آموزش دادن از تکنیک crossValidation استفاده میشود\n",
    "\n",
    "<img src=\"images/cross_validation.png\" width=\"600\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35327592439588484"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "LR = LinearRegression()\n",
    "cv_scores = cross_val_score(LR, x, y, cv=5)\n",
    "np.mean(cv_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### ridge regression * \n",
    "\n",
    "alpha * sigma (i=1, n) a_i be tavane 2\n",
    "\n",
    "alpha ye zarib mesle K dar KNN hast ke dar tabe hazine (OSL) zarb mishe \n",
    "\n",
    "age kheyli ziad bashe underfitting be vojod miad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.6805716228976983\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "                                    x, y, test_size=0.3)\n",
    "ridge = Ridge(alpha=0.1, normalize=True)\n",
    "ridge.fit(x_train, y_train)\n",
    "print(\"Score:\", ridge.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### lasso regression * \n",
    "alpha * sigma (i=1, n) ghadre motlaghe a_i\n",
    "\n",
    "same as ridge\n",
    "\n",
    "maziati ke dare ine ke zarayebe feature haye kam ahamiat ro 0 mikone (feature selection)\n",
    "\n",
    "\n",
    "masalan dar entekhabe feature dar morede datae economic tedade roozaye aftabi feature alaki i hast vali saderat data mohemie\n",
    "\n",
    "pas roozaye aftabi be fuck mire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.6141459148049635\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso(alpha=0.1, normalize=True)\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "                                    x, y, test_size=0.3)\n",
    "lasso.fit(x_train, y_train)\n",
    "print(\"Score:\", lasso.score(x_test, y_test))\n",
    "\n",
    "plt.figure(dpi=140, figsize=(9, 5))\n",
    "plt.plot(range(13), lasso.coef_)\n",
    "plt.xticks(range(13), boston.feature_names)\n",
    "plt.ylabel(\"Coefficent\")\n",
    "plt.xlabel(\"Features name\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification\n",
    "\n",
    "tasavor kon 99 ta az 100 ta email k miad vagheie va 1 doone spame\n",
    "pas age ye model alaki ham besazi bazam ba score 0.99 dare kar mikone\n",
    "\n",
    "too in zamine vase inke betoonim score ro peyda konim bayad az\n",
    "**confusion Matrix** estefade konim\n",
    "<div>\n",
    "<img src=\"confusion_mat.png\" width=\"600\">\n",
    "<img src=\"confusion_formula.png\" width=\"600\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# breast cancer dataset\n",
    "\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "bcd = load_breast_cancer()\n",
    "x = bcd.data\n",
    "y = bcd.target\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "                                    x, y, test_size=0.2, random_state=42)\n",
    "\n",
    "KNN = KNeighborsClassifier(n_neighbors=8)\n",
    "KNN.fit(x_train, y_train)\n",
    "y_pred = KNN.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[39  4]\n",
      " [ 1 70]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.91      0.94        43\n",
      "           1       0.95      0.99      0.97        71\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.96      0.95      0.95       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# confusion matrix baraye amaliate bala\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred, [0, 1])\n",
    "print(cm)\n",
    "cr = classification_report(y_test, y_pred)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### GridSearch  *\n",
    "بر اساس هایپر پارامتر های مختلف چند بار مدل رو ترین و تست میکنه و بهترین حالت رو خروجی میده\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Gaussian Naive Bayesian * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(x_train, y_train)\n",
    "y_pred = gnb.predict(x_test)\n",
    "gnb.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}