Notebooks/Confusion_Matrix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Confusion Matrix\n",
    "\n",
    "Confusion Matrix (*Matriz de confusão*) é uma ferramenta muito usada para avaliação de modelos de classificação em Machine Learning.\n",
    "\n",
    "As matrizes de confusão são uma tabela que mostra as frequências de classificação para cada classe do modelo.\n",
    "\n",
    "* **Verdadeiro positivo (*True positive -- TP*)**: ocorre quando no conjunto real, a classe que estamos buscando foi prevista corretamente.\n",
    "\n",
    "* **Falso positivo (*false positive -- FP*)** : Ocorre quando no conjunto real, a classe que estamos buscando prever foi prevista incorretamente.\n",
    "\n",
    "* **Falso verdadeiro (*true negative -- TN*)** : Ocorre quando no conjunto real, a classe que não estamos buscando prever foi prevista corretamente. \n",
    "\n",
    "* **Falso negativo (*false negative -- FN*)** : Ocorre quando no conjunto real, a classe que não estamos buscando prever foi prevista incorretamente.\n",
    "\n",
    "![image](https://i.ibb.co/H4Jdjs3/Screenshot-from-2020-09-22-16-02-35.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 30)\n",
    "dados = load_breast_cancer()\n",
    "x = pd.DataFrame(dados.data, columns=[dados.feature_names])\n",
    "y = pd.Series(dados.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>1.0950</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>8.589</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>3.398</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>4.585</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>3.445</td>\n",
       "      <td>27.23</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>5.438</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mean radius mean texture mean perimeter mean area mean smoothness  \\\n",
       "0       17.99        10.38         122.80    1001.0         0.11840   \n",
       "1       20.57        17.77         132.90    1326.0         0.08474   \n",
       "2       19.69        21.25         130.00    1203.0         0.10960   \n",
       "3       11.42        20.38          77.58     386.1         0.14250   \n",
       "4       20.29        14.34         135.10    1297.0         0.10030   \n",
       "\n",
       "  mean compactness mean concavity mean concave points mean symmetry  \\\n",
       "0          0.27760         0.3001             0.14710        0.2419   \n",
       "1          0.07864         0.0869             0.07017        0.1812   \n",
       "2          0.15990         0.1974             0.12790        0.2069   \n",
       "3          0.28390         0.2414             0.10520        0.2597   \n",
       "4          0.13280         0.1980             0.10430        0.1809   \n",
       "\n",
       "  mean fractal dimension radius error texture error perimeter error  \\\n",
       "0                0.07871       1.0950        0.9053           8.589   \n",
       "1                0.05667       0.5435        0.7339           3.398   \n",
       "2                0.05999       0.7456        0.7869           4.585   \n",
       "3                0.09744       0.4956        1.1560           3.445   \n",
       "4                0.05883       0.7572        0.7813           5.438   \n",
       "\n",
       "  area error smoothness error compactness error concavity error  \\\n",
       "0     153.40         0.006399           0.04904         0.05373   \n",
       "1      74.08         0.005225           0.01308         0.01860   \n",
       "2      94.03         0.006150           0.04006         0.03832   \n",
       "3      27.23         0.009110           0.07458         0.05661   \n",
       "4      94.44         0.011490           0.02461         0.05688   \n",
       "\n",
       "  concave points error symmetry error fractal dimension error worst radius  \\\n",
       "0              0.01587        0.03003                0.006193        25.38   \n",
       "1              0.01340        0.01389                0.003532        24.99   \n",
       "2              0.02058        0.02250                0.004571        23.57   \n",
       "3              0.01867        0.05963                0.009208        14.91   \n",
       "4              0.01885        0.01756                0.005115        22.54   \n",
       "\n",
       "  worst texture worst perimeter worst area worst smoothness worst compactness  \\\n",
       "0         17.33          184.60     2019.0           0.1622            0.6656   \n",
       "1         23.41          158.80     1956.0           0.1238            0.1866   \n",
       "2         25.53          152.50     1709.0           0.1444            0.4245   \n",
       "3         26.50           98.87      567.7           0.2098            0.8663   \n",
       "4         16.67          152.20     1575.0           0.1374            0.2050   \n",
       "\n",
       "  worst concavity worst concave points worst symmetry worst fractal dimension  \n",
       "0          0.7119               0.2654         0.4601                 0.11890  \n",
       "1          0.2416               0.1860         0.2750                 0.08902  \n",
       "2          0.4504               0.2430         0.3613                 0.08758  \n",
       "3          0.6869               0.2575         0.6638                 0.17300  \n",
       "4          0.4000               0.1625         0.2364                 0.07678  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando como estão distribuida as váriaveis dessa coluna.\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia :: 0.9707602339181286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Separando os dados entre treino e teste:\n",
    "X_treino, X_teste, Y_treino, Y_teste = train_test_split(x, y, test_size=0.3, random_state=9)\n",
    "\n",
    "#Criação do modelo:\n",
    "modelo = LogisticRegression(C=95, penalty='l2', max_iter=5000)\n",
    "modelo.fit(X_treino, Y_treino)\n",
    "\n",
    "#Score:\n",
    "resultado = modelo.score(X_teste, Y_teste)\n",
    "print('Acurácia ::', resultado)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizando a predição\n",
    "predicao = modelo.predict(X_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0 1 1 0 1 1 0 0 0 1 1 1 0 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 0 1 1 1 1 "
     ]
    }
   ],
   "source": [
    "for valores in predicao:\n",
    "    print(valores, end=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 58   4]\n",
      " [  1 108]]\n"
     ]
    }
   ],
   "source": [
    "# Conferindo os valores que foram previstos:\n",
    "from sklearn.metrics import confusion_matrix\n",
    "matriz = confusion_matrix(Y_teste, predicao)\n",
    "print(matriz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora de acordo com a tabela que foi vista acima, podemos verificar os valores, que foram previstos que nesse caso foram:\n",
    "\n",
    "* **Verdadeiro Positivos** : 58\n",
    "* **Falso Positivo** : 4\n",
    "* **Falso Negativo** : 1\n",
    "* **Verdadeiro Negativo** : 108"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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