NaiveBayesClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from csv import reader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go on to importing the data. To do so, we'll utilize the pandas library to manipulate our data. The code for importing and previewing the data is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./Mushroom_Train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below confirms that all columns are categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 23 columns</p>\n",
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      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df = df.select_dtypes(include=['object']).copy()\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we go any further, let's make sure there aren't any null values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [class, cap-shape, cap-surface, cap-color, bruises, odor, gill-attachment, gill-spacing, gill-size, gill-color, stalk-shape, stalk-root, stalk-surface-above-ring, stalk-surface-below-ring, stalk-color-above-ring, stalk-color-below-ring, veil-type, veil-color, ring-number, ring-type, spore-print-color, population, habitat]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 23 columns]"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[obj_df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values in the dataframe, however column \"stalk-root\" has some(31%) missing values (\"?\"). As a result, we apply the most-common value method to assign a value to this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    3756\n",
       "?    1991\n",
       "e    1120\n",
       "c     541\n",
       "r     192\n",
       "Name: stalk-root, dtype: int64"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"stalk-root\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    5747\n",
       "e    1120\n",
       "c     541\n",
       "r     192\n",
       "Name: stalk-root, dtype: int64"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"stalk-root\"].replace({\"?\": \"b\"}, inplace=True)\n",
    "obj_df[\"stalk-root\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that there are no null values in the data, we can consider encoding options for categorical values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll create a mapping dictionary with each column to process as well as a dictionary of values to translate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "remove \"veil-type\" column because it has 1 unique value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       p\n",
       "1       p\n",
       "2       p\n",
       "3       p\n",
       "4       p\n",
       "       ..\n",
       "7595    p\n",
       "7596    p\n",
       "7597    p\n",
       "7598    p\n",
       "7599    p\n",
       "Name: veil-type, Length: 7600, dtype: object"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df.pop(\"veil-type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "{'class': {'e': 0, 'p': 1}, 'cap-shape': {'b': 0, 'c': 1, 'f': 2, 'k': 3, 's': 4, 'x': 5}, 'cap-surface': {'f': 0, 'g': 1, 's': 2, 'y': 3}, 'cap-color': {'b': 0, 'c': 1, 'e': 2, 'g': 3, 'n': 4, 'p': 5, 'r': 6, 'u': 7, 'w': 8, 'y': 9}, 'bruises': {'f': 0, 't': 1}, 'odor': {'a': 0, 'c': 1, 'f': 2, 'l': 3, 'm': 4, 'n': 5, 'p': 6, 's': 7, 'y': 8}, 'gill-attachment': {'a': 0, 'f': 1}, 'gill-spacing': {'c': 0, 'w': 1}, 'gill-size': {'b': 0, 'n': 1}, 'gill-color': {'b': 0, 'e': 1, 'g': 2, 'h': 3, 'k': 4, 'n': 5, 'o': 6, 'p': 7, 'r': 8, 'u': 9, 'w': 10, 'y': 11}, 'stalk-shape': {'e': 0, 't': 1}, 'stalk-root': {'b': 0, 'c': 1, 'e': 2, 'r': 3}, 'stalk-surface-above-ring': {'f': 0, 'k': 1, 's': 2, 'y': 3}, 'stalk-surface-below-ring': {'f': 0, 'k': 1, 's': 2, 'y': 3}, 'stalk-color-above-ring': {'b': 0, 'c': 1, 'e': 2, 'g': 3, 'n': 4, 'o': 5, 'p': 6, 'w': 7, 'y': 8}, 'stalk-color-below-ring': {'b': 0, 'c': 1, 'e': 2, 'g': 3, 'n': 4, 'o': 5, 'p': 6, 'w': 7, 'y': 8}, 'veil-color': {'n': 0, 'o': 1, 'w': 2, 'y': 3}, 'ring-number': {'n': 0, 'o': 1, 't': 2}, 'ring-type': {'e': 0, 'f': 1, 'l': 2, 'n': 3, 'p': 4}, 'spore-print-color': {'b': 0, 'h': 1, 'k': 2, 'n': 3, 'o': 4, 'r': 5, 'u': 6, 'w': 7, 'y': 8}, 'population': {'a': 0, 'c': 1, 'n': 2, 's': 3, 'v': 4, 'y': 5}, 'habitat': {'d': 0, 'g': 1, 'l': 2, 'm': 3, 'p': 4, 'u': 5, 'w': 6}}\n"
     ]
    }
   ],
   "source": [
    "colNum = obj_df.shape[1] #number of columns(features)\n",
    "print(colNum)\n",
    "colNames = list(obj_df.columns) #names of columns(features)\n",
    "\n",
    "def encodeCol(col_index):   \n",
    "\n",
    "    names = np.unique(obj_df[colNames[col_index]].values)\n",
    "    keys = list(range(0,len(names)))\n",
    "    encoded_names = dict(zip(names, keys))\n",
    "\n",
    "    return encoded_names\n",
    "\n",
    "# n = colNames[11]\n",
    "# k = encodeCol(11)\n",
    "# print(k)\n",
    "\n",
    "encoded_all = {}\n",
    "# encoded_all[n] = k\n",
    "# print(encoded_all)\n",
    "\n",
    "\n",
    "i=0\n",
    "while i < colNum:\n",
    "    encoded_all[colNames[i]] = encodeCol(i)\n",
    "    i+=1\n",
    "\n",
    "print(encoded_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using replace, convert the columns to numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7600 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class  cap-shape  cap-surface  cap-color  bruises  odor  \\\n",
       "0         1          5            2          4        1     6   \n",
       "1         0          5            2          9        1     0   \n",
       "2         0          0            2          8        1     3   \n",
       "3         1          5            3          8        1     6   \n",
       "4         0          5            2          3        0     5   \n",
       "...     ...        ...          ...        ...      ...   ...   \n",
       "7595      1          3            2          2        0     2   \n",
       "7596      1          3            2          4        0     7   \n",
       "7597      0          5            2          4        1     5   \n",
       "7598      1          3            2          4        0     2   \n",
       "7599      1          3            3          2        0     2   \n",
       "\n",
       "      gill-attachment  gill-spacing  gill-size  gill-color  ...  \\\n",
       "0                   1             0          1           4  ...   \n",
       "1                   1             0          0           4  ...   \n",
       "2                   1             0          0           5  ...   \n",
       "3                   1             0          1           5  ...   \n",
       "4                   1             1          0           4  ...   \n",
       "...               ...           ...        ...         ...  ...   \n",
       "7595                1             0          1           0  ...   \n",
       "7596                1             0          1           0  ...   \n",
       "7597                1             0          0          10  ...   \n",
       "7598                1             0          1           0  ...   \n",
       "7599                1             0          1           0  ...   \n",
       "\n",
       "      stalk-surface-above-ring  stalk-surface-below-ring  \\\n",
       "0                            2                         2   \n",
       "1                            2                         2   \n",
       "2                            2                         2   \n",
       "3                            2                         2   \n",
       "4                            2                         2   \n",
       "...                        ...                       ...   \n",
       "7595                         1                         2   \n",
       "7596                         2                         1   \n",
       "7597                         2                         2   \n",
       "7598                         1                         1   \n",
       "7599                         1                         1   \n",
       "\n",
       "      stalk-color-above-ring  stalk-color-below-ring  veil-color  ring-number  \\\n",
       "0                          7                       7           2            1   \n",
       "1                          7                       7           2            1   \n",
       "2                          7                       7           2            1   \n",
       "3                          7                       7           2            1   \n",
       "4                          7                       7           2            1   \n",
       "...                      ...                     ...         ...          ...   \n",
       "7595                       7                       7           2            1   \n",
       "7596                       6                       6           2            1   \n",
       "7597                       7                       7           2            2   \n",
       "7598                       7                       6           2            1   \n",
       "7599                       6                       6           2            1   \n",
       "\n",
       "      ring-type  spore-print-color  population  habitat  \n",
       "0             4                  2           3        5  \n",
       "1             4                  3           2        1  \n",
       "2             4                  3           2        3  \n",
       "3             4                  2           3        5  \n",
       "4             0                  3           0        1  \n",
       "...         ...                ...         ...      ...  \n",
       "7595          0                  7           4        2  \n",
       "7596          0                  7           4        0  \n",
       "7597          4                  7           4        4  \n",
       "7598          0                  7           4        4  \n",
       "7599          0                  7           4        4  \n",
       "\n",
       "[7600 rows x 22 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from collections import defaultdict\n",
    "# d = defaultdict(preprocessing.LabelEncoder)\n",
    "\n",
    "df_train = obj_df.replace(encoded_all)\n",
    "\n",
    "df_train\n",
    "# df_train = df(map(lambda x: d[x.name].fit_transform(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the first column represents the class, while the remaining columns represent the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After encoding the dataset, we use Naive Bayes to classify it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train.drop('class', axis=1)\n",
    "y_train = df_train['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_csv('./Mushroom_Test.csv')\n",
    "\n",
    "df_test[\"stalk-root\"].replace({\"?\": \"b\"}, inplace=True)\n",
    "\n",
    "colNum = df_test.shape[1] #number of columns(features)\n",
    "print(colNum)\n",
    "colNames = list(df_test.columns) #names of columns(features)\n",
    "\n",
    "def encodeCol(col_index):   \n",
    "    names = np.unique(df_test[colNames[col_index]].values)\n",
    "    keys = list(range(0,len(names)))\n",
    "    encoded_names = dict(zip(names, keys))\n",
    "\n",
    "    return encoded_names\n",
    "\n",
    "# n = colNames[11]\n",
    "# k = encodeCol(11)\n",
    "# print(k)\n",
    "\n",
    "encoded_all = {}\n",
    "# encoded_all[n] = k\n",
    "# print(encoded_all)\n",
    "\n",
    "\n",
    "i = 0\n",
    "while i < colNum:\n",
    "    encoded_all[colNames[i]]=encodeCol(i)\n",
    "    i += 1\n",
    "\n",
    "# print(encoded_all)\n",
    "df_test = df_test.replace(encoded_all)\n",
    "df_test.head()\n",
    "\n",
    "# df_test = df_test.apply(lambda x: d[x.name].transform(x))\n",
    "df_test.pop(\"veil-type\")\n",
    "X_test = df_test.drop('class', axis=1)\n",
    "y_test = df_test['class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building the classifier:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes Rule\n",
    "$ P(class|data) = \\frac{P(data|class)P(class)}{P(data)}$\n",
    "\n",
    "- $P(data|class)$ : Likelihood\n",
    "- $P(class)$ : Prior Probability\n",
    "- $P(class|data)$ : Posterior Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GNaiveBayesClassifier:\n",
    "    def priorProb(self, X, y):\n",
    "\n",
    "        self.prior = (X.groupby(y).apply(lambda x: len(x)) / self.rows).to_numpy()\n",
    "\n",
    "        return self.prior\n",
    "    \n",
    "    def statParamteres(self, X, y):\n",
    "\n",
    "        self.mean = X.groupby(y).apply(np.mean).to_numpy()\n",
    "        self.var = X.groupby(y).apply(np.var).to_numpy()\n",
    "        return self.mean, self.var\n",
    "\n",
    "    def densGauss(self, class_i, x):     \n",
    " \n",
    "        mean = self.mean[class_i]\n",
    "        var = self.var[class_i]\n",
    "        pmf = np.exp((-1/2)*((x-mean)**2) / (2 * var)) / np.sqrt(2 * np.pi * var)\n",
    "\n",
    "        return pmf\n",
    "\n",
    "    def postProb(self, x):\n",
    "\n",
    "        posteriors = []\n",
    "\n",
    "        for i in range(self.count):\n",
    "\n",
    "            prior = np.log(self.prior[i]) \n",
    "            conditional = np.sum(np.log(self.densGauss(i, x))) \n",
    "            posterior = prior + conditional\n",
    "            posteriors.append(posterior)\n",
    "\n",
    "        return self.classes[np.argmax(posteriors)]\n",
    "        \n",
    "    def train(self, X, y):\n",
    "        self.classes = np.unique(y)\n",
    "        self.count = len(self.classes)\n",
    "        self.num_feature = X.shape[1]\n",
    "        self.rows = X.shape[0]\n",
    "        \n",
    "        self.statParamteres(X, y)\n",
    "        self.priorProb(X, y)\n",
    "        \n",
    "    def predict(self, X):\n",
    "        preds = [self.postProb(f) for f in X.to_numpy()]\n",
    "        return preds\n",
    "\n",
    "    def accuracy(self, y_test, y_pred):\n",
    "        accuracy = np.sum(y_test == y_pred) / len(y_test)\n",
    "        return accuracy\n",
    "    \n",
    "    def confusionMatrix(self, predicted, classes): #confusion matrix \n",
    "        mat = np.zeros((2, 2), dtype=np.int32)\n",
    "\n",
    "        predictedNP = np.array(predicted)\n",
    "        classesNP = np.array(classes)\n",
    "\n",
    "        for i in range(len(predictedNP)):\n",
    "            if predictedNP[i] == 1: \n",
    "                if classesNP[i] == 1:#TP\n",
    "                    mat[0][0] += 1\n",
    "                elif classesNP[i] == 0:#FP\n",
    "                    mat[0][1] += 1\n",
    "            elif predictedNP[i] == 0:\n",
    "                if classesNP[i] == 1:#TN\n",
    "                    mat[1][0] += 1\n",
    "                elif classesNP[i] == 0:#FN\n",
    "                    mat[1][1] += 1\n",
    "    \n",
    "        print(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GNaiveBayesClassifier()\n",
    "model.train(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 382,
     "metadata": {},
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    }
   ],
   "source": [
    "predicted = model.predict(X_test)\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9561068702290076"
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     "execution_count": 383,
     "metadata": {},
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   ],
   "source": [
    "model.accuracy(y_test, predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[260   8]\n",
      " [ 15 241]]\n"
     ]
    }
   ],
   "source": [
    "model.confusionMatrix(predicted, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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