Predicting Heart Disease risk using combined UCI data and machine learning.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Heart Disease risk using combined UCI data and machine learning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n",
    "**Stratoudakis Alexandros<br>\n",
    "Kazantzakis Nikolaos<br>\n",
    "Kotsa Christina**<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fall 2021<br>\n",
    "Final group project of the course \"Introduction to Data Science and Machine Learning I\" that was undertaken during the fall semester of 2021.<br>\n",
    "*Instructor: Prof. G.P. Tsironis* <br>\n",
    "*University of Crete, Physics Department, Greece*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "1. Introduction\n",
    "2. Setup\n",
    "3. Pre-processing<br>\n",
    "&nbsp;3.1 Data Exploration<br>\n",
    "&nbsp;3.2 Data Transformation<br>\n",
    "&nbsp;3.3 Data Visualization<br>\n",
    "&nbsp;3.4 Scaling\n",
    "4. Models' Training<br>\n",
    "&nbsp;4.1 Models with default hyperparameters<br>\n",
    "&nbsp;&emsp;4.1.1 Models' Training and evaluation<br>\n",
    "&nbsp;&emsp;4.1.2 Voting Classifier<br>\n",
    "&nbsp;4.2 Models with fine-tuned hyperparameters<br>\n",
    "&nbsp;&emsp;4.2.1 Fine-tuning of hyperparameters<br>\n",
    "&nbsp;&emsp;4.2.2 Fine-Tuned Models' Training and evaluation<br>\n",
    "&nbsp;&emsp;4.2.3 Fine-Tuned Voting Classifier<br>\n",
    "5. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heart disease is the number one cause of death globally (except Africa). It is estimeted that up to 90% of heart disease is preventable. It is ,therefore, very important to develop methods of prevention for such cases. In this project we use a combined dataset (fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved  from https://www.kaggle.com/fedesoriano/heart-failure-prediction) to create machine learning models that can predict heart disease risk based on a patient's results to simple medical tests. <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#common imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import classification_report\n",
    "import seaborn as sns\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize = 14)\n",
    "mpl.rc('xtick', labelsize = 12)\n",
    "mpl.rc('ytick', labelsize = 12)\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message = \"^internal gelsd\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv(\"heart.csv\", thousands=',')#Load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Age             918 non-null    int64  \n",
      " 1   Sex             918 non-null    object \n",
      " 2   ChestPainType   918 non-null    object \n",
      " 3   RestingBP       918 non-null    int64  \n",
      " 4   Cholesterol     918 non-null    int64  \n",
      " 5   FastingBS       918 non-null    int64  \n",
      " 6   RestingECG      918 non-null    object \n",
      " 7   MaxHR           918 non-null    int64  \n",
      " 8   ExerciseAngina  918 non-null    object \n",
      " 9   Oldpeak         918 non-null    float64\n",
      " 10  ST_Slope        918 non-null    object \n",
      " 11  HeartDisease    918 non-null    int64  \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 86.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The #12 column is our target variable; 1 stands for the existence of heart disease and 0 for the absence of it. The 11 remaining columns are our features. There are 918 instances and no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeartDisease    0.553377\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data[[\"HeartDisease\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is slightly imbalanced in favor of patients with heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>339</td>\n",
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       "      <td>237</td>\n",
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       "      <td>ASY</td>\n",
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      "text/plain": [
       "    Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "0    40   M           ATA        140          289          0     Normal   \n",
       "1    49   F           NAP        160          180          0     Normal   \n",
       "2    37   M           ATA        130          283          0         ST   \n",
       "3    48   F           ASY        138          214          0     Normal   \n",
       "4    54   M           NAP        150          195          0     Normal   \n",
       "5    39   M           NAP        120          339          0     Normal   \n",
       "6    45   F           ATA        130          237          0     Normal   \n",
       "7    54   M           ATA        110          208          0     Normal   \n",
       "8    37   M           ASY        140          207          0     Normal   \n",
       "9    48   F           ATA        120          284          0     Normal   \n",
       "10   37   F           NAP        130          211          0     Normal   \n",
       "11   58   M           ATA        136          164          0         ST   \n",
       "12   39   M           ATA        120          204          0     Normal   \n",
       "13   49   M           ASY        140          234          0     Normal   \n",
       "14   42   F           NAP        115          211          0         ST   \n",
       "\n",
       "    MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0     172              N      0.0       Up             0  \n",
       "1     156              N      1.0     Flat             1  \n",
       "2      98              N      0.0       Up             0  \n",
       "3     108              Y      1.5     Flat             1  \n",
       "4     122              N      0.0       Up             0  \n",
       "5     170              N      0.0       Up             0  \n",
       "6     170              N      0.0       Up             0  \n",
       "7     142              N      0.0       Up             0  \n",
       "8     130              Y      1.5     Flat             1  \n",
       "9     120              N      0.0       Up             0  \n",
       "10    142              N      0.0       Up             0  \n",
       "11     99              Y      2.0     Flat             1  \n",
       "12    145              N      0.0       Up             0  \n",
       "13    140              Y      1.0     Flat             1  \n",
       "14    137              N      0.0       Up             0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(15) #Let us take a look on the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some features are qualitative variables. For our machine learning algorithms to work, we will transform them to quantitative variables by assigning integers to each possible value. In addition, we observe that the 'Cholesterol' feature has some 'zero' values (that represent null values). For our purposes, we shall leave them as they are, as it helps with performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Digitize the data\n",
    "sexmap={'M':1,'F':0}\n",
    "chestpainmap={'ATA':1,'TA':0,'NAP':2,'ASY':6}\n",
    "restingecgmap={'Normal':0,'ST':1,'LVH':2} #Dictionaries of our chosen digitization\n",
    "exanginamap={'N':0,'Y':1}\n",
    "stslopemap={'Up':2,'Flat':1,'Down':0}\n",
    "datadig=data.replace({'Sex': sexmap,'ChestPainType': chestpainmap,'RestingECG':restingecgmap,'ExerciseAngina':exanginamap,'ST_Slope':stslopemap})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
       "0     40    1              1        140          289          0           0   \n",
       "1     49    0              2        160          180          0           0   \n",
       "2     37    1              1        130          283          0           1   \n",
       "3     48    0              6        138          214          0           0   \n",
       "4     54    1              2        150          195          0           0   \n",
       "..   ...  ...            ...        ...          ...        ...         ...   \n",
       "913   45    1              0        110          264          0           0   \n",
       "914   68    1              6        144          193          1           0   \n",
       "915   57    1              6        130          131          0           0   \n",
       "916   57    0              1        130          236          0           2   \n",
       "917   38    1              2        138          175          0           0   \n",
       "\n",
       "     MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
       "0      172               0      0.0         2             0  \n",
       "1      156               0      1.0         1             1  \n",
       "2       98               0      0.0         2             0  \n",
       "3      108               1      1.5         1             1  \n",
       "4      122               0      0.0         2             0  \n",
       "..     ...             ...      ...       ...           ...  \n",
       "913    132               0      1.2         1             1  \n",
       "914    141               0      3.4         1             1  \n",
       "915    115               1      1.2         1             1  \n",
       "916    174               0      0.0         1             1  \n",
       "917    173               0      0.0         2             0  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datadig  #see how the qualificatives have been quantified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=\"HeartDisease\"\n",
    "x=datadig.drop(target,axis=1) #pands dataframe of features\n",
    "y=datadig[target] #column vector of target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split #split 'em all\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2,random_state=42) #train-test ratio: 80-20."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we shall create a correlation matrix to see how our features correlate with our target variable and with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dc=np.abs(datadig.corr())\n",
    "dc=datadig.corr()\n",
    "plt.figure(figsize=(20, 10))\n",
    "sns.heatmap(dc,robust=True,cmap='magma',annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x3600 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x3600 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols2=['Age','RestingBP','Cholesterol', 'MaxHR','Oldpeak']\n",
    "cols1=[i for i in datadig.columns if i not in cols2]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(15,50))\n",
    "for i in range(len(cols2)):\n",
    "        col = cols2[i]\n",
    "        sns.histplot(x=col, data=data, hue=target, multiple=\"dodge\", palette=\"flare\", alpha=1, ax=axes[i])\n",
    "        axes[i].legend([f\"Heart Disease: {datadig[datadig[target]==1.0][col].mean():.2f}\", \n",
    "                        f\"No Heart Disease: {datadig[datadig[target]==0.0][col].mean():.2f}\"],\n",
    "                   fontsize=18,\n",
    "                   title=f\"{col} average\",\n",
    "                   title_fontsize=20,\n",
    "                   loc ='upper right')\n",
    "axes[0].legend([])\n",
    "plt.show()\n",
    "fig,axes=plt.subplots(nrows=7, ncols=1, figsize=(15,50))\n",
    "for i in range(len(cols1)):\n",
    "        col = cols1[i]\n",
    "        sns.countplot(x=col, data=data, hue=target, palette=\"flare\",saturation=1,linewidth=1,ax=axes[i], edgecolor='black')\n",
    "        axes[i].legend(title='Heart Disease', loc ='upper right')\n",
    "axes[0].legend([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our machine learning algorithms to work, we need to scale the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "scaler.fit(xtrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain_tf=scaler.transform(xtrain)\n",
    "xtest_tf=scaler.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24506731,  0.54060477, -0.8167297 , ..., -0.83846064,\n",
       "        -0.09706109, -2.25746543],\n",
       "       [-1.8862362 ,  0.54060477, -0.8167297 , ..., -0.83846064,\n",
       "        -0.83628643,  1.07985506],\n",
       "       [ 0.25099346,  0.54060477,  0.8948466 , ...,  1.19266183,\n",
       "         0.08774524, -0.58880518],\n",
       "       ...,\n",
       "       [ 0.67843939,  0.54060477,  0.8948466 , ...,  1.19266183,\n",
       "         0.45735791,  1.07985506],\n",
       "       [ 0.67843939,  0.54060477,  0.8948466 , ...,  1.19266183,\n",
       "        -0.83628643,  1.07985506],\n",
       "       [-1.45879027, -1.84978019,  0.8948466 , ..., -0.83846064,\n",
       "         1.01177691, -0.58880518]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain_tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Models' Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the classifiers\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression , SGDClassifier, Perceptron, PassiveAggressiveClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Models with default hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we will use some of scikit-learn's classifiers with their default hyperparameter values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.1 Models' Training and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_clf = RandomForestClassifier(random_state=42) #define classifiers\n",
    "extra_trees_clf = ExtraTreesClassifier( random_state=42)    \n",
    "svm_clf = LinearSVC(random_state=42)\n",
    "mlp_clf = MLPClassifier(random_state=42)\n",
    "softmax_reg_clf = LogisticRegression(multi_class=\"multinomial\", solver=\"lbfgs\", random_state=42)\n",
    "KNeighbors_clf=KNeighborsClassifier(n_jobs=-1)\n",
    "Perceptron_clf=Perceptron()\n",
    "AdaBoost_clf=AdaBoostClassifier(random_state=42)\n",
    "GradientBoosting_clf=GradientBoostingClassifier(random_state=42)\n",
    "Bagging_clf=BaggingClassifier(random_state=42)\n",
    "tree_clf=DecisionTreeClassifier(random_state=42)\n",
    "log_reg_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "SGDC_clf=SGDClassifier(random_state=42)\n",
    "GP_clf=GaussianProcessClassifier(random_state=42)\n",
    "PassiveAggresive_clf=PassiveAggressiveClassifier(random_state=42)\n",
    "Non_linear_svm_clf=SVC(kernel=\"poly\",probability=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the RandomForestClassifier(random_state=42)\n",
      "Training the ExtraTreesClassifier(random_state=42)\n",
      "Training the LinearSVC(random_state=42)\n",
      "Training the MLPClassifier(random_state=42)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexs\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:985: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\alexs\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the LogisticRegression(multi_class='multinomial', random_state=42)\n",
      "Training the LogisticRegression(random_state=42, solver='liblinear')\n",
      "Training the KNeighborsClassifier(n_jobs=-1)\n",
      "Training the Perceptron()\n",
      "Training the AdaBoostClassifier(random_state=42)\n",
      "Training the GradientBoostingClassifier(random_state=42)\n",
      "Training the BaggingClassifier(random_state=42)\n",
      "Training the DecisionTreeClassifier(random_state=42)\n",
      "Training the SGDClassifier(random_state=42)\n",
      "Training the GaussianProcessClassifier(random_state=42)\n",
      "Training the SVC(kernel='poly', probability=True, random_state=42)\n"
     ]
    }
   ],
   "source": [
    "estimators = [random_forest_clf,\n",
    "              extra_trees_clf,\n",
    "              svm_clf, \n",
    "              mlp_clf,\n",
    "              softmax_reg_clf,\n",
    "              log_reg_clf,\n",
    "              KNeighbors_clf,\n",
    "              Perceptron_clf, \n",
    "              AdaBoost_clf, \n",
    "              GradientBoosting_clf, \n",
    "              Bagging_clf, \n",
    "              tree_clf, \n",
    "              SGDC_clf, \n",
    "              GP_clf,\n",
    "              #PassiveAggresive_clf,\n",
    "              Non_linear_svm_clf\n",
    "             ] #array of classifiers\n",
    "for estimator in estimators:\n",
    "    print(\"Training the\", estimator)\n",
    "    estimator.fit(xtrain_tf, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold #Cross-Validation with k-fold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cv=KFold(n_splits=5, shuffle=True ,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RandomForestClassifier(random_state=42) cross validated f1-scores on the train set are [0.92941176 0.82716049 0.87861272 0.875      0.85333333]\n",
      "F1-Score: 0.873 ± 0.034\n",
      "Accuracy: 0.860 ± 0.035\n",
      "\n",
      "The ExtraTreesClassifier(random_state=42) cross validated f1-scores on the train set are [0.93491124 0.8        0.87573964 0.86419753 0.80272109]\n",
      "F1-Score: 0.856 ± 0.050\n",
      "Accuracy: 0.843 ± 0.050\n",
      "\n",
      "The LinearSVC(random_state=42) cross validated f1-scores on the train set are [0.9112426  0.81987578 0.87209302 0.89171975 0.84076433]\n",
      "F1-Score: 0.867 ± 0.033\n",
      "Accuracy: 0.853 ± 0.035\n",
      "\n",
      "The MLPClassifier(random_state=42) cross validated f1-scores on the train set are [0.92121212 0.83435583 0.87209302 0.87116564 0.81290323]\n",
      "F1-Score: 0.862 ± 0.037\n",
      "Accuracy: 0.847 ± 0.038\n",
      "\n",
      "The LogisticRegression(multi_class='multinomial', random_state=42) cross validated f1-scores on the train set are [0.9112426  0.83435583 0.87209302 0.89171975 0.84076433]\n",
      "F1-Score: 0.870 ± 0.029\n",
      "Accuracy: 0.856 ± 0.031\n",
      "\n",
      "The LogisticRegression(random_state=42, solver='liblinear') cross validated f1-scores on the train set are [0.9112426  0.83435583 0.87209302 0.89171975 0.84076433]\n",
      "F1-Score: 0.870 ± 0.029\n",
      "Accuracy: 0.856 ± 0.031\n",
      "\n",
      "The KNeighborsClassifier(n_jobs=-1) cross validated f1-scores on the train set are [0.91017964 0.83333333 0.86585366 0.84810127 0.80519481]\n",
      "F1-Score: 0.853 ± 0.035\n",
      "Accuracy: 0.838 ± 0.036\n",
      "\n",
      "The Perceptron() cross validated f1-scores on the train set are [0.87150838 0.81481481 0.86956522 0.86792453 0.8427673 ]\n",
      "F1-Score: 0.853 ± 0.022\n",
      "Accuracy: 0.832 ± 0.020\n",
      "\n",
      "The AdaBoostClassifier(random_state=42) cross validated f1-scores on the train set are [0.90909091 0.81707317 0.85380117 0.87179487 0.84415584]\n",
      "F1-Score: 0.859 ± 0.031\n",
      "Accuracy: 0.845 ± 0.034\n",
      "\n",
      "The GradientBoostingClassifier(random_state=42) cross validated f1-scores on the train set are [0.93413174 0.84146341 0.88505747 0.8516129  0.85      ]\n",
      "F1-Score: 0.872 ± 0.034\n",
      "Accuracy: 0.858 ± 0.036\n",
      "\n",
      "The BaggingClassifier(random_state=42) cross validated f1-scores on the train set are [0.89570552 0.78709677 0.84337349 0.82352941 0.82666667]\n",
      "F1-Score: 0.835 ± 0.035\n",
      "Accuracy: 0.824 ± 0.035\n",
      "\n",
      "The DecisionTreeClassifier(random_state=42) cross validated f1-scores on the train set are [0.85889571 0.78709677 0.77647059 0.75       0.76712329]\n",
      "F1-Score: 0.788 ± 0.038\n",
      "Accuracy: 0.771 ± 0.040\n",
      "\n",
      "The SGDClassifier(random_state=42) cross validated f1-scores on the train set are [0.87356322 0.75609756 0.76836158 0.88050314 0.77987421]\n",
      "F1-Score: 0.812 ± 0.054\n",
      "Accuracy: 0.786 ± 0.063\n",
      "\n",
      "The GaussianProcessClassifier(random_state=42) cross validated f1-scores on the train set are [0.91666667 0.82208589 0.84662577 0.85534591 0.79470199]\n",
      "F1-Score: 0.847 ± 0.041\n",
      "Accuracy: 0.834 ± 0.041\n",
      "\n",
      "The SVC(kernel='poly', probability=True, random_state=42) cross validated f1-scores on the train set are [0.91017964 0.82634731 0.86549708 0.86956522 0.78666667]\n",
      "F1-Score: 0.852 ± 0.042\n",
      "Accuracy: 0.836 ± 0.041\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for estimator in estimators: #cross validation of all individual classifiers\n",
    "    score = cross_val_score(estimator, xtrain_tf, ytrain, scoring='f1', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "    print(\"The \"+str(estimator)+\" cross validated f1-scores on the train set are \"+str(score))\n",
    "    print('F1-Score: %.3f ± %.3f' % (np.mean(score), np.std(score)))\n",
    "    score = cross_val_score(estimator, xtrain_tf, ytrain, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "    print('Accuracy: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will see how the classifiers perform on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report of  RandomForestClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.90      0.87        77\n",
      "           1       0.92      0.89      0.90       107\n",
      "\n",
      "    accuracy                           0.89       184\n",
      "   macro avg       0.89      0.89      0.89       184\n",
      "weighted avg       0.89      0.89      0.89       184\n",
      "\n",
      "Classification report of  ExtraTreesClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.92      0.90        77\n",
      "           1       0.94      0.91      0.92       107\n",
      "\n",
      "    accuracy                           0.91       184\n",
      "   macro avg       0.91      0.91      0.91       184\n",
      "weighted avg       0.91      0.91      0.91       184\n",
      "\n",
      "Classification report of  LinearSVC(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.88      0.84        77\n",
      "           1       0.91      0.84      0.87       107\n",
      "\n",
      "    accuracy                           0.86       184\n",
      "   macro avg       0.85      0.86      0.86       184\n",
      "weighted avg       0.86      0.86      0.86       184\n",
      "\n",
      "Classification report of  MLPClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.90      0.86        77\n",
      "           1       0.92      0.87      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.88      0.88      0.88       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  LogisticRegression(multi_class='multinomial', random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.88      0.84        77\n",
      "           1       0.91      0.84      0.87       107\n",
      "\n",
      "    accuracy                           0.86       184\n",
      "   macro avg       0.85      0.86      0.86       184\n",
      "weighted avg       0.86      0.86      0.86       184\n",
      "\n",
      "Classification report of  LogisticRegression(random_state=42, solver='liblinear')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.88      0.84        77\n",
      "           1       0.91      0.84      0.87       107\n",
      "\n",
      "    accuracy                           0.86       184\n",
      "   macro avg       0.85      0.86      0.86       184\n",
      "weighted avg       0.86      0.86      0.86       184\n",
      "\n",
      "Classification report of  KNeighborsClassifier(n_jobs=-1)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.86      0.82        77\n",
      "           1       0.89      0.84      0.87       107\n",
      "\n",
      "    accuracy                           0.85       184\n",
      "   macro avg       0.84      0.85      0.85       184\n",
      "weighted avg       0.85      0.85      0.85       184\n",
      "\n",
      "Classification report of  Perceptron()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.78      0.75        77\n",
      "           1       0.83      0.78      0.80       107\n",
      "\n",
      "    accuracy                           0.78       184\n",
      "   macro avg       0.77      0.78      0.77       184\n",
      "weighted avg       0.78      0.78      0.78       184\n",
      "\n",
      "Classification report of  AdaBoostClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.90      0.83        77\n",
      "           1       0.92      0.81      0.86       107\n",
      "\n",
      "    accuracy                           0.85       184\n",
      "   macro avg       0.85      0.85      0.85       184\n",
      "weighted avg       0.86      0.85      0.85       184\n",
      "\n",
      "Classification report of  GradientBoostingClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.88      0.86        77\n",
      "           1       0.91      0.87      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  BaggingClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.91      0.85        77\n",
      "           1       0.93      0.83      0.88       107\n",
      "\n",
      "    accuracy                           0.86       184\n",
      "   macro avg       0.86      0.87      0.86       184\n",
      "weighted avg       0.87      0.86      0.86       184\n",
      "\n",
      "Classification report of  DecisionTreeClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.84      0.77        77\n",
      "           1       0.87      0.76      0.81       107\n",
      "\n",
      "    accuracy                           0.79       184\n",
      "   macro avg       0.79      0.80      0.79       184\n",
      "weighted avg       0.81      0.79      0.79       184\n",
      "\n",
      "Classification report of  SGDClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.87      0.82        77\n",
      "           1       0.90      0.82      0.86       107\n",
      "\n",
      "    accuracy                           0.84       184\n",
      "   macro avg       0.84      0.85      0.84       184\n",
      "weighted avg       0.85      0.84      0.84       184\n",
      "\n",
      "Classification report of  GaussianProcessClassifier(random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.90      0.85        77\n",
      "           1       0.92      0.84      0.88       107\n",
      "\n",
      "    accuracy                           0.86       184\n",
      "   macro avg       0.86      0.87      0.86       184\n",
      "weighted avg       0.87      0.86      0.86       184\n",
      "\n",
      "Classification report of  SVC(kernel='poly', probability=True, random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for estimator in estimators:#classification reports\n",
    "    print(\"Classification report of \", estimator)\n",
    "    print(classification_report(ytest,estimator.predict(xtest_tf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RandomForestClassifier(random_state=42) f1-score on the test set is:\n",
      "0.905\n",
      "The ExtraTreesClassifier(random_state=42) f1-score on the test set is:\n",
      "0.924\n",
      "The LinearSVC(random_state=42) f1-score on the test set is:\n",
      "0.874\n",
      "The MLPClassifier(random_state=42) f1-score on the test set is:\n",
      "0.894\n",
      "The LogisticRegression(multi_class='multinomial', random_state=42) f1-score on the test set is:\n",
      "0.874\n",
      "The LogisticRegression(random_state=42, solver='liblinear') f1-score on the test set is:\n",
      "0.874\n",
      "The KNeighborsClassifier(n_jobs=-1) f1-score on the test set is:\n",
      "0.865\n",
      "The Perceptron() f1-score on the test set is:\n",
      "0.802\n",
      "The AdaBoostClassifier(random_state=42) f1-score on the test set is:\n",
      "0.861\n",
      "The GradientBoostingClassifier(random_state=42) f1-score on the test set is:\n",
      "0.890\n",
      "The BaggingClassifier(random_state=42) f1-score on the test set is:\n",
      "0.877\n",
      "The DecisionTreeClassifier(random_state=42) f1-score on the test set is:\n",
      "0.810\n",
      "The SGDClassifier(random_state=42) f1-score on the test set is:\n",
      "0.859\n",
      "The GaussianProcessClassifier(random_state=42) f1-score on the test set is:\n",
      "0.878\n",
      "The SVC(kernel='poly', probability=True, random_state=42) f1-score on the test set is:\n",
      "0.888\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "for estimator in estimators:\n",
    "    f1_test=f1_score(ytest,estimator.predict(xtest_tf))\n",
    "    print(\"The \"+str(estimator)+\" f1-score on the test set is:\")\n",
    "    print(\"%.3f\" % f1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrices\n",
    "fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15,20))\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "for estimator, ax in zip(estimators, axes.flatten()):\n",
    "    plot_confusion_matrix(estimator, \n",
    "                          xtest_tf, \n",
    "                          ytest, \n",
    "                          ax=ax, \n",
    "                          cmap='cividis',\n",
    "                          normalize='true'\n",
    "                         )\n",
    "    ax.title.set_text(type(estimator).__name__)\n",
    "                          \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.2 Voting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the above models performed well (considering that we have not yet fine-tuned any hyperparameter) others...not that great.<br>What we will try to do now, is create a Voting classifier consisting of our best models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hard Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "named_estimators = [\n",
    "    (\"random_forest_clf\", random_forest_clf),\n",
    "    (\"extra_trees_clf\", extra_trees_clf),\n",
    "    #(\"svm_clf\",svm_clf),\n",
    "    #(\"mlp_clf\", mlp_clf),\n",
    "    #(\"softmax_reg_clf\",softmax_reg_clf),\n",
    "    #(\"log_reg_clf\", log_reg_clf),\n",
    "    #(\"KNeighbors_clf\", KNeighbors_clf),\n",
    "    #(\"Perceptron_clf\", Perceptron_clf),\n",
    "    #(\"AdaBoost_clf\", AdaBoost_clf),\n",
    "    (\"GradientBoosting_clf\", GradientBoosting_clf),\n",
    "    #(\"Bagging_clf\", Bagging_clf),\n",
    "    #(\"tree_clf\",tree_clf),\n",
    "    #(\"SGDC_clf\", SGDC_clf),\n",
    "    #(\"GP_clf\", GP_clf),\n",
    "    #(\"Non_linear_svm_clf\", Non_linear_svm_clf)\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf',\n",
       "                              RandomForestClassifier(random_state=42)),\n",
       "                             ('extra_trees_clf',\n",
       "                              ExtraTreesClassifier(random_state=42)),\n",
       "                             ('GradientBoosting_clf',\n",
       "                              GradientBoostingClassifier(random_state=42))])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf = VotingClassifier(named_estimators) #Training the voting classifier\n",
    "voting_clf.fit(xtrain_tf, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Hard Voting classifier cross validated f1-scores on the train set are [0.94047619 0.83229814 0.88372093 0.88050314 0.8496732 ]\n",
      "F1-Score: 0.877 ± 0.037\n",
      "\n",
      "The Hard Voting classifier cross validated accuracy-scores on the train set are [0.93197279 0.81632653 0.86394558 0.8707483  0.84246575]\n",
      "Accuracy: 0.865 ± 0.038\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#cross validation of Hard votin clf\n",
    "score = cross_val_score(voting_clf, xtrain_tf, ytrain, scoring='f1', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "print(\"The Hard Voting classifier cross validated f1-scores on the train set are \"+str(score))\n",
    "print('F1-Score: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")\n",
    "\n",
    "score = cross_val_score(voting_clf, xtrain_tf, ytrain, scoring='accuracy', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "print(\"The Hard Voting classifier cross validated accuracy-scores on the train set are \"+str(score))\n",
    "print('Accuracy: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Soft Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_estimators_soft = [\n",
    "    (\"random_forest_clf\", random_forest_clf),\n",
    "    (\"extra_trees_clf\", extra_trees_clf),\n",
    "    #(\"svm_clf\",svm_clf),\n",
    "    #(\"mlp_clf\", mlp_clf),\n",
    "    #(\"softmax_reg_clf\",softmax_reg_clf),\n",
    "    #(\"log_reg_clf\", log_reg_clf),\n",
    "    #(\"KNeighbors_clf\", KNeighbors_clf),\n",
    "    #(\"Perceptron_clf\", Perceptron_clf),\n",
    "    #(\"AdaBoost_clf\", AdaBoost_clf),\n",
    "    (\"GradientBoosting_clf\", GradientBoosting_clf),\n",
    "    #(\"Bagging_clf\", Bagging_clf),\n",
    "    #(\"tree_clf\",tree_clf),\n",
    "    #(\"SGDC_clf\", SGDC_clf),\n",
    "    #(\"GP_clf\", GP_clf),\n",
    "    #(\"Non_linear_svm_clf\", Non_linear_svm_clf)\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf',\n",
       "                              RandomForestClassifier(random_state=42)),\n",
       "                             ('extra_trees_clf',\n",
       "                              ExtraTreesClassifier(random_state=42)),\n",
       "                             ('GradientBoosting_clf',\n",
       "                              GradientBoostingClassifier(random_state=42))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf_soft = VotingClassifier(named_estimators_soft, voting='soft') #Training the softvoting classifier\n",
    "voting_clf_soft.fit(xtrain_tf, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Soft Voting classifier cross validated f1-scores on the train set are [0.93413174 0.84662577 0.89534884 0.87341772 0.83870968]\n",
      "F1-Score: 0.878 ± 0.035\n",
      "\n",
      "The Soft Voting classifier cross validated accuracy-scores on the train set are [0.92517007 0.82993197 0.87755102 0.86394558 0.82876712]\n",
      "Accuracy: 0.865 ± 0.036\n",
      "\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(voting_clf_soft, xtrain_tf, ytrain, scoring='f1', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "print(\"The Soft Voting classifier cross validated f1-scores on the train set are \"+str(score))\n",
    "print('F1-Score: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")\n",
    "\n",
    "score = cross_val_score(voting_clf_soft, xtrain_tf, ytrain, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "print(\"The Soft Voting classifier cross validated accuracy-scores on the train set are \"+str(score))\n",
    "print('Accuracy: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets try them with the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report of  VotingClassifier(estimators=[('random_forest_clf',\n",
      "                              RandomForestClassifier(random_state=42)),\n",
      "                             ('extra_trees_clf',\n",
      "                              ExtraTreesClassifier(random_state=42)),\n",
      "                             ('GradientBoosting_clf',\n",
      "                              GradientBoostingClassifier(random_state=42))])\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.88      0.88        77\n",
      "           1       0.92      0.91      0.91       107\n",
      "\n",
      "    accuracy                           0.90       184\n",
      "   macro avg       0.89      0.89      0.89       184\n",
      "weighted avg       0.90      0.90      0.90       184\n",
      "\n",
      "Classification report of  VotingClassifier(estimators=[('random_forest_clf',\n",
      "                              RandomForestClassifier(random_state=42)),\n",
      "                             ('extra_trees_clf',\n",
      "                              ExtraTreesClassifier(random_state=42)),\n",
      "                             ('GradientBoosting_clf',\n",
      "                              GradientBoostingClassifier(random_state=42))],\n",
      "                 voting='soft')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.90      0.88        77\n",
      "           1       0.92      0.91      0.92       107\n",
      "\n",
      "    accuracy                           0.90       184\n",
      "   macro avg       0.90      0.90      0.90       184\n",
      "weighted avg       0.90      0.90      0.90       184\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification report of \", voting_clf)\n",
    "print(classification_report(ytest,voting_clf.predict(xtest_tf)))\n",
    "print(\"Classification report of \", voting_clf_soft)\n",
    "print(classification_report(ytest,voting_clf_soft.predict(xtest_tf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The VotingClassifier(estimators=[('random_forest_clf',\n",
      "                              RandomForestClassifier(random_state=42)),\n",
      "                             ('extra_trees_clf',\n",
      "                              ExtraTreesClassifier(random_state=42)),\n",
      "                             ('GradientBoosting_clf',\n",
      "                              GradientBoostingClassifier(random_state=42))]) f1-score on the test set is:\n",
      "0.888\n",
      "The VotingClassifier(estimators=[('random_forest_clf',\n",
      "                              RandomForestClassifier(random_state=42)),\n",
      "                             ('extra_trees_clf',\n",
      "                              ExtraTreesClassifier(random_state=42)),\n",
      "                             ('GradientBoosting_clf',\n",
      "                              GradientBoostingClassifier(random_state=42))],\n",
      "                 voting='soft') f1-score on the test set is:\n",
      "0.888\n"
     ]
    }
   ],
   "source": [
    "voters=[voting_clf, voting_clf_soft]\n",
    "for voter in voters:\n",
    "    f1_test=f1_score(ytest,estimator.predict(xtest_tf))\n",
    "    print(\"The \"+str(voter)+\" f1-score on the test set is:\")\n",
    "    print(\"%.3f\" % f1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,5))\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "for voter, ax in zip(voters, axes.flatten()):\n",
    "    plot_confusion_matrix(voter, \n",
    "                          xtest_tf, \n",
    "                          ytest, \n",
    "                          ax=ax, \n",
    "                          cmap='cividis',\n",
    "                          normalize='true'\n",
    "                         )\n",
    "    ax.title.set_text(type(voter).__name__)\n",
    "                          \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Models with fine-tuned hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although our combined model does well, we can try to make it even better by fine-tuning the classifiers' hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.1 Fine-tuning of hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part we shall implement the means of randomized search cv to efficiently find the best hyperparameters for the models we trained before.<br><br>**WARNING**:This chapter (4.2.1) takes ridiculously long time to run!!! It is strogly suggested to skip it and head directly to chapter 4.2.2 where the computed hyperparameters are used on the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-121-8fabb05ed2fc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;34m'max_leaf_nodes'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m },cv=cv, return_train_score=False, n_iter=1000,n_jobs=-1,random_state=42)\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mrs_RFC\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxtrain_tf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mytrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    734\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 736\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    737\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1527\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1528\u001b[0m         \u001b[1;34m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1529\u001b[1;33m         evaluate_candidates(ParameterSampler(\n\u001b[0m\u001b[0;32m   1530\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1531\u001b[0m             random_state=self.random_state))\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    706\u001b[0m                               n_splits, n_candidates, n_candidates * n_splits))\n\u001b[0;32m    707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 708\u001b[1;33m                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),\n\u001b[0m\u001b[0;32m    709\u001b[0m                                                        \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m                                                        \u001b[0mtrain\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1040\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1041\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1042\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1043\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1044\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    919\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    920\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 921\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    922\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    923\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    541\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    543\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    432\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 434\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "rs_RFC=RandomizedSearchCV(RandomForestClassifier(random_state=42),{\n",
    "    'n_estimators': range(10,301,1),#the number of trees in the forest (default=100)\n",
    "    'criterion':['gini', 'entropy'], \n",
    "    'max_depth': [None, ] + list(range(1,50,1)),\n",
    "    'max_features':['auto','sqrt','log2'],\n",
    "    'max_leaf_nodes': [None, ] + list(range(1,50,1))\n",
    "},cv=cv, return_train_score=False, n_iter=1000,n_jobs=-1,random_state=42)\n",
    "rs_RFC.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8678221973720996\n",
      "best parameters:  {'n_estimators': 258, 'max_leaf_nodes': 35, 'max_features': 'log2', 'max_depth': 35, 'criterion': 'entropy'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_RFC.best_score_)\n",
    "print(\"best parameters: \",rs_RFC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Extra Trees Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=ExtraTreesClassifier(random_state=42), n_iter=300,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [None, 1, 2, 3, 4, 5, 6, 7,\n",
       "                                                      8, 9, 10, 11, 12, 13, 14,\n",
       "                                                      15, 16, 17, 18, 19, 20,\n",
       "                                                      21, 22, 23, 24, 25, 26,\n",
       "                                                      27, 28, 29, ...],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'max_leaf_nodes': [None, 1, 2, 3, 4, 5,\n",
       "                                                           6, 7, 8, 9, 10, 11,\n",
       "                                                           12, 13, 14, 15, 16,\n",
       "                                                           17, 18, 19, 20, 21,\n",
       "                                                           22, 23, 24, 25, 26,\n",
       "                                                           27, 28, 29, ...],\n",
       "                                        'n_estimators': range(10, 301)},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_ETC=RandomizedSearchCV(ExtraTreesClassifier(random_state=42),{\n",
    "    'n_estimators': range(10,301,1),\n",
    "    'criterion':['gini', 'entropy'], #to measure the quality of a split\n",
    "    'max_depth': [None, ] + list(range(1,50,1)), #the maximum depth of the tree\n",
    "    'max_features':['auto', 'sqrt', 'log2'],\n",
    "    'max_leaf_nodes': [None, ] + list(range(1,50,1))\n",
    "},cv=cv, return_train_score=False, n_iter=300,random_state=42,scoring='accuracy',n_jobs=-1)\n",
    "\n",
    "rs_ETC.fit(xtrain_tf,ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8637126083310036\n",
      "best parameters:  {'n_estimators': 71, 'max_leaf_nodes': 45, 'max_features': 'log2', 'max_depth': 35, 'criterion': 'gini'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_ETC.best_score_)\n",
    "print(\"best parameters: \",rs_ETC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### LinearSVC Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=LinearSVC(random_state=42), n_iter=1000, n_jobs=-1,\n",
       "                   param_distributions={'C': array([ 0.1      ,  0.1019902,  0.1039804, ..., 19.9960196, 19.9980098,\n",
       "       20.       ]),\n",
       "                                        'class_weight': [None, 'dict',\n",
       "                                                         'balanced'],\n",
       "                                        'dual': [True, False],\n",
       "                                        'fit_intercept': [True, False],\n",
       "                                        'loss': ['hinge', 'squared_hinge'],\n",
       "                                        'max_iter': range(700, 1500, 5),\n",
       "                                        'multi_class': ['ovr',\n",
       "                                                        'crammer_singer'],\n",
       "                                        'penalty': ['l1', 'l2']},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_LSVC=RandomizedSearchCV(LinearSVC(random_state=42),{\n",
    "    'penalty':['l1', 'l2'],\n",
    "    'loss':['hinge', 'squared_hinge'],\n",
    "    'dual' :[True, False],\n",
    "    'C':np.linspace(0.1,20,10000),\n",
    "    'class_weight':[None, 'dict','balanced'],\n",
    "    'multi_class': ['ovr', 'crammer_singer'],\n",
    "    'max_iter': range(700,1500,5),\n",
    "    'fit_intercept': [True, False]\n",
    "   }, cv=cv, return_train_score=False, n_jobs=-1 ,n_iter=1000, random_state=42,scoring='accuracy')\n",
    "\n",
    "rs_LSVC.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8623520641133166\n",
      "best parameters:  {'penalty': 'l2', 'multi_class': 'ovr', 'max_iter': 1075, 'loss': 'hinge', 'fit_intercept': True, 'dual': True, 'class_weight': 'balanced', 'C': 1.3558155815581558}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_LSVC.best_score_)\n",
    "print(\"best parameters: \",rs_LSVC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:471: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=MLPClassifier(random_state=42), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'activation': ['relu', 'tanh',\n",
       "                                                       'logistic', 'identity'],\n",
       "                                        'alpha': [1e-05, 0.0001, 0.001, 0.01,\n",
       "                                                  0.1, 1, 10, 100, 1000,\n",
       "                                                  10000],\n",
       "                                        'early_stopping': [True, False],\n",
       "                                        'hidden_layer_sizes': [(100, 0),\n",
       "                                                               (100, 1),\n",
       "                                                               (100, 2),\n",
       "                                                               (100, 3),\n",
       "                                                               (100,...\n",
       "                                                               (100, 7),\n",
       "                                                               (100, 8),\n",
       "                                                               (100, 9),\n",
       "                                                               (100, 10),\n",
       "                                                               (100, 11),\n",
       "                                                               (100, 12),\n",
       "                                                               (100, 13),\n",
       "                                                               (100, 14),\n",
       "                                                               (100, 15),\n",
       "                                                               (100, 16),\n",
       "                                                               (100, 17),\n",
       "                                                               (100, 18),\n",
       "                                                               (100, 19),\n",
       "                                                               (100, 20),\n",
       "                                                               (100, 21),\n",
       "                                                               (100, 22),\n",
       "                                                               (100, 23),\n",
       "                                                               (100, 24),\n",
       "                                                               (100, 25),\n",
       "                                                               (100, 26),\n",
       "                                                               (100, 27),\n",
       "                                                               (100, 28),\n",
       "                                                               (100, 29), ...],\n",
       "                                        'learning_rate': ['constant',\n",
       "                                                          'invscaling',\n",
       "                                                          'adaptive'],\n",
       "                                        'max_iter': range(100, 300),\n",
       "                                        'solver': ['adam', 'sgd', 'lbfgs']},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_mplC=RandomizedSearchCV(MLPClassifier(random_state=42),{\n",
    "                'hidden_layer_sizes': [(100,x) for x in range(100)],\n",
    "                'solver': ['adam', 'sgd', 'lbfgs'],\n",
    "                'activation': ['relu', 'tanh', 'logistic', 'identity'],\n",
    "                'alpha': [0.00001,0.0001,0.001,0.01,0.1,1,10,100,1000,10000],\n",
    "                'learning_rate': ['constant', 'invscaling', 'adaptive'],\n",
    "                'early_stopping': [True, False],\n",
    "                'max_iter': range(100,300)\n",
    "                     },\n",
    "                     cv=cv, return_train_score=False, n_jobs=-1,n_iter=100,scoring='accuracy',random_state=42)\n",
    "rs_mplC.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.859640294473954\n",
      "best parameters:  {'solver': 'lbfgs', 'max_iter': 129, 'learning_rate': 'invscaling', 'hidden_layer_sizes': (100, 41), 'early_stopping': True, 'alpha': 10, 'activation': 'relu'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_mplC.best_score_)\n",
    "print(\"best parameters: \",rs_mplC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "             estimator=LogisticRegression(random_state=42), n_jobs=-1,\n",
       "             param_grid={'C': array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06,\n",
       "       1.e+07, 1.e+08, 1.e+09]),\n",
       "                         'dual': [True, False], 'max_iter': range(50, 150),\n",
       "                         'multi_class': ['auto', 'ovr', 'multinomial'],\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet'],\n",
       "                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
       "                                    'saga']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_LogR=GridSearchCV(LogisticRegression(random_state=42),{\n",
    "    'penalty': ['l1','l2', 'elasticnet'],\n",
    "    'dual':[True, False],\n",
    "    'C':np.logspace(-1,9,11),\n",
    "    'solver':['newton-cg', 'lbfgs',  'liblinear', 'sag', 'saga'],\n",
    "    'multi_class':['auto', 'ovr', 'multinomial'],\n",
    "    'max_iter':range(50,150),\n",
    "},cv=cv, return_train_score=False, \n",
    "                     #n_iter=20,\n",
    "                     n_jobs=-1,scoring='accuracy',\n",
    "                     #random_state=42\n",
    "                    )\n",
    "\n",
    "rs_LogR.fit(xtrain_tf,ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8582704314602554\n",
      "best parameters:  {'C': 10.0, 'dual': True, 'max_iter': 53, 'multi_class': 'auto', 'penalty': 'l2', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_LogR.best_score_)\n",
    "print(\"best parameters: \",rs_LogR.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### K - Nearest Neighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=KNeighborsClassifier(n_jobs=-1), n_iter=300,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'algorithm': ['auto', 'ball_tree',\n",
       "                                                      'kd_tree', 'brute'],\n",
       "                                        'leaf_size': array([1.]),\n",
       "                                        'n_neighbors': range(1, 80),\n",
       "                                        'weights': ['uniform', 'distance']},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_KNC=RandomizedSearchCV(KNeighborsClassifier(n_jobs=-1),{\n",
    "                    'n_neighbors':range(1,80,1),\n",
    "                    'weights':['uniform', 'distance'],\n",
    "                    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "                    'leaf_size':np.linspace(1,100,1)\n",
    "                     },\n",
    "                     cv=cv, return_train_score=False, n_iter=300,n_jobs=-1,random_state=42,scoring='accuracy')\n",
    "rs_KNC.fit(xtrain_tf,ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8637126083310036\n",
      "best parameters:  {'weights': 'distance', 'n_neighbors': 15, 'leaf_size': 1.0, 'algorithm': 'brute'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_KNC.best_score_)\n",
    "print(\"best parameters: \",rs_KNC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=Perceptron(random_state=42), n_iter=300, n_jobs=-1,\n",
       "                   param_distributions={'alpha': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01]),\n",
       "                                        'class_weight': [None, 'dict',\n",
       "                                                         'balanced'],\n",
       "                                        'early_stopping': [True, False],\n",
       "                                        'max_iter': range(500, 1500),\n",
       "                                        'penalty': ['l2', 'l1', 'elasticnet',\n",
       "                                                    None],\n",
       "                                        'shuffle': [True, False]},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_pctron=RandomizedSearchCV(Perceptron(random_state=42),{\n",
    "    'penalty':['l2','l1','elasticnet',None],\n",
    "    'alpha':np.logspace(-5,1,7), #default=0.0001\n",
    "    'max_iter':range(500,1500),\n",
    "    'shuffle':[True, False],\n",
    "    'class_weight':[None,'dict','balanced'],\n",
    "    'early_stopping': [True, False],\n",
    "}, cv=cv, return_train_score=False, n_jobs=-1, n_iter=300,random_state=42,scoring='accuracy')\n",
    "\n",
    "rs_pctron.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8350573105954708\n",
      "best parameters:  {'shuffle': True, 'penalty': 'l1', 'max_iter': 1351, 'early_stopping': True, 'class_weight': None, 'alpha': 0.01}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_pctron.best_score_)\n",
    "print(\"best parameters: \",rs_pctron.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adaptive Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=AdaBoostClassifier(base_estimator=RandomForestClassifier(random_state=42)),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'algorithm': ['SAMME', 'SAMME.R'],\n",
       "                                        'learning_rate': array([ 1.        ,  1.18367347,  1.36734694,  1.55102041,  1.73469388,\n",
       "        1.91836735,  2.10204082,  2.28571429,  2.46938776,  2.653061...\n",
       "        4.67346939,  4.85714286,  5.04081633,  5.2244898 ,  5.40816327,\n",
       "        5.59183673,  5.7755102 ,  5.95918367,  6.14285714,  6.32653061,\n",
       "        6.51020408,  6.69387755,  6.87755102,  7.06122449,  7.24489796,\n",
       "        7.42857143,  7.6122449 ,  7.79591837,  7.97959184,  8.16326531,\n",
       "        8.34693878,  8.53061224,  8.71428571,  8.89795918,  9.08163265,\n",
       "        9.26530612,  9.44897959,  9.63265306,  9.81632653, 10.        ]),\n",
       "                                        'n_estimators': range(5, 201, 5)},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_ABC=RandomizedSearchCV(AdaBoostClassifier(base_estimator=RandomForestClassifier(random_state=42)),{\n",
    "    'n_estimators': range(5,201,5),#the number of trees in the forest (default=50)\n",
    "    'algorithm' : ['SAMME','SAMME.R'], \n",
    "    'learning_rate':np.linspace(1,10,50),\n",
    "},cv=cv, return_train_score=False,random_state=42,n_jobs=-1,n_iter=100,scoring='accuracy')\n",
    "rs_ABC.fit(xtrain_tf,ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8664616531544125\n",
      "best parameters:  {'n_estimators': 35, 'learning_rate': 5.591836734693878, 'algorithm': 'SAMME'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_ABC.best_score_)\n",
    "print(\"best parameters: \",rs_ABC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=GradientBoostingClassifier(random_state=42),\n",
       "                   n_iter=1000, n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['friedman_mse',\n",
       "                                                      'squared_error', 'mse',\n",
       "                                                      'mae'],\n",
       "                                        'loss': ['deviance', 'exponential'],\n",
       "                                        'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                      10, 11, 12, 13, 14, 15,\n",
       "                                                      16, 17, 18, 19, 20, 21,\n",
       "                                                      22, 23, 24, 25, 26, 27,\n",
       "                                                      28, 29, 30, ...],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'max_leaf_nodes': range(1, 40),\n",
       "                                        'n_estimators': range(10, 400),\n",
       "                                        'n_iter_no_change': [None, 1, 2, 3, 4,\n",
       "                                                             5, 6, 7, 8, 9],\n",
       "                                        'subsample': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_GBC=RandomizedSearchCV(GradientBoostingClassifier(random_state=42),{\n",
    "                    'loss': ['deviance', 'exponential'],#The loss function to be optimized\n",
    "                    'n_estimators': range(10,400,10),\n",
    "                    'subsample': np.linspace(0,1,11),\n",
    "                    'criterion': ['friedman_mse', 'squared_error', 'mse', 'mae'],\n",
    "                    'max_depth': list(range(1,40,1)),\n",
    "                    'max_leaf_nodes': range(1,40),\n",
    "                    'n_iter_no_change':[None, ] + list(range(1,10,1)),\n",
    "                    'max_features': ['auto', 'sqrt', 'log2']\n",
    "                     },\n",
    "                     cv=cv, return_train_score=False, n_jobs=-1, n_iter=300,random_state=42,scoring='accuracy')\n",
    "rs_GBC.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8705619233994968\n",
      "best parameters:  {'subsample': 0.30000000000000004, 'n_iter_no_change': None, 'n_estimators': 80, 'max_leaf_nodes': 36, 'max_features': 'log2', 'max_depth': 2, 'loss': 'exponential', 'criterion': 'mse'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_GBC.best_score_)\n",
    "print(\"best parameters: \",rs_GBC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=BaggingClassifier(random_state=42), n_iter=30,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'base_estimator': [RandomForestClassifier(random_state=42)],\n",
       "                                        'bootstrap': [True, False],\n",
       "                                        'n_estimators': range(0, 100)},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_BC=RandomizedSearchCV(BaggingClassifier(random_state=42),{\n",
    "                    'n_estimators': range(0,100),\n",
    "                    'base_estimator': [RandomForestClassifier(random_state=42)],\n",
    "                    'bootstrap':[True,False]\n",
    "                    \n",
    "                    },\n",
    "                    \n",
    "                     cv=cv, return_train_score=False,n_iter=30,n_jobs=-1,random_state=42,scoring='accuracy')\n",
    "rs_BC.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8664523343584009\n",
      "best parameters:  {'n_estimators': 84, 'bootstrap': True, 'base_estimator': RandomForestClassifier(random_state=42)}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_BC.best_score_)\n",
    "print(\"best parameters: \",rs_BC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=DecisionTreeClassifier(random_state=42),\n",
       "                   n_iter=1000, n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [None, 1, 2, 3, 4, 5, 6, 7,\n",
       "                                                      8, 9, 10, 11, 12, 13, 14,\n",
       "                                                      15, 16, 17, 18, 19, 20,\n",
       "                                                      21, 22, 23, 24, 25, 26,\n",
       "                                                      27, 28, 29, ...],\n",
       "                                        'max_features': ['auto', 'sqrt', 'log2',\n",
       "                                                         None],\n",
       "                                        'max_leaf_nodes': [None, 1, 2, 3, 4, 5,\n",
       "                                                           6, 7, 8, 9, 10, 11,\n",
       "                                                           12, 13, 14, 15, 16,\n",
       "                                                           17, 18, 19, 20, 21,\n",
       "                                                           22, 23, 24, 25, 26,\n",
       "                                                           27, 28, 29, ...],\n",
       "                                        'splitter': ['best', 'random']},\n",
       "                   random_state=42)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_DTC=RandomizedSearchCV(DecisionTreeClassifier(random_state=42),{\n",
    "                'criterion': ['gini', 'entropy'],\n",
    "                'splitter':['best', 'random'],\n",
    "                'max_depth': [None, ] + list(range(1,60,1)),\n",
    "                'max_leaf_nodes':[None, ] + list(range(1,60,1)),\n",
    "                'max_features': ['auto', 'sqrt', 'log2',None],         \n",
    "},cv=cv, return_train_score=False, n_jobs=-1, n_iter=1000,random_state=42)\n",
    "\n",
    "rs_DTC.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8514583915758085\n",
      "best parameters:  {'splitter': 'random', 'max_leaf_nodes': 57, 'max_features': None, 'max_depth': 6, 'criterion': 'entropy'}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_DTC.best_score_)\n",
    "print(\"best parameters: \",rs_DTC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Stochastic Gradient Descent Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=SGDClassifier(random_state=42), n_iter=2000,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'alpha': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n",
       "       1.e+03, 1.e+04, 1.e+05]),\n",
       "                                        'early_stopping': [True, False],\n",
       "                                        'eta0': range(1, 10),\n",
       "                                        'fit_intercept': [True, False],\n",
       "                                        'learning_rate': ['constant', 'optimal',\n",
       "                                                          'invscaling',\n",
       "                                                          'adaptive'],\n",
       "                                        'loss': ['hinge', 'log',\n",
       "                                                 'modified_huber',\n",
       "                                                 'squared_hinge',\n",
       "                                                 'perceptron'],\n",
       "                                        'max_iter': range(700, 1300),\n",
       "                                        'penalty': ['l2', 'l1', 'elasticnet'],\n",
       "                                        'shuffle': [True, False]},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_SGD=RandomizedSearchCV(SGDClassifier(random_state=42),{\n",
    "                'loss': ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'],\n",
    "                'penalty': ['l2', 'l1', 'elasticnet'],\n",
    "                'max_iter':range(700,1300),\n",
    "                'alpha':np.logspace(-5,5,11),\n",
    "                'early_stopping':[True,False],\n",
    "                'shuffle':[True,False],\n",
    "                'fit_intercept': [True,False],\n",
    "                'eta0':range(1,10),\n",
    "                'learning_rate': ['constant','optimal','invscaling','adaptive']\n",
    "},cv=cv, return_train_score=False, n_jobs=-1, n_iter=2000,random_state=42,scoring='accuracy')\n",
    "\n",
    "rs_SGD.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8582797502562668\n",
      "best parameters:  {'shuffle': False, 'penalty': 'l1', 'max_iter': 1210, 'loss': 'hinge', 'learning_rate': 'adaptive', 'fit_intercept': True, 'eta0': 3, 'early_stopping': False, 'alpha': 0.01}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_SGD.best_score_)\n",
    "print(\"best parameters: \",rs_SGD.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gaussian Procces Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=GaussianProcessClassifier(random_state=42),\n",
       "                   n_iter=180, n_jobs=-1,\n",
       "                   param_distributions={'max_iter_predict': range(50, 200),\n",
       "                                        'multi_class': ['one_vs_rest',\n",
       "                                                        'one_vs_one'],\n",
       "                                        'n_restarts_optimizer': range(0, 10)},\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_GP=RandomizedSearchCV(GaussianProcessClassifier(random_state=42),{\n",
    "                'multi_class': ['one_vs_rest', 'one_vs_one'],\n",
    "                'max_iter_predict': range(50,200),\n",
    "                'n_restarts_optimizer': range(0,10),\n",
    "                \n",
    "},cv=cv, return_train_score=False, n_jobs=-1, n_iter=180,random_state=42,scoring='accuracy')\n",
    "\n",
    "rs_GP.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.8337247227658187\n",
      "best parameters:  {'n_restarts_optimizer': 1, 'multi_class': 'one_vs_rest', 'max_iter_predict': 140}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_GP.best_score_)\n",
    "print(\"best parameters: \",rs_GP.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Non-Linear SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=SVC(random_state=42), n_iter=5000, n_jobs=-1,\n",
       "                   param_distributions={'C': array([0.00000000e+00, 1.00010001e-04, 2.00020002e-04, ...,\n",
       "       9.99799980e-01, 9.99899990e-01, 1.00000000e+00]),\n",
       "                                        'degree': range(1, 10),\n",
       "                                        'gamma': ['scale', 'auto'],\n",
       "                                        'kernel': ['linear', 'poly', 'rbf',\n",
       "                                                   'sigmoid']},\n",
       "                   random_state=42)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_SVC=RandomizedSearchCV(SVC(random_state=42),{\n",
    "                'C':np.linspace(0,1,10000),\n",
    "                'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "                'degree': range(1,10),\n",
    "                'gamma': ['scale','auto'],\n",
    "                \n",
    "},cv=cv, return_train_score=False, n_jobs=-1, n_iter=5000,random_state=42)\n",
    "\n",
    "rs_SVC.fit(xtrain_tf,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.861000838691641\n",
      "best parameters:  {'kernel': 'rbf', 'gamma': 'auto', 'degree': 8, 'C': 0.16931693169316933}\n"
     ]
    }
   ],
   "source": [
    "print(\"best score: \",rs_SVC.best_score_)\n",
    "print(\"best parameters: \",rs_SVC.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.2 Fine-Tuned Models' Training and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From now on, we will follow the same procces as in chapter 4.1 exept we will use the hyperparameters we computed in chapter 4.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_clfF = RandomForestClassifier(n_estimators=258,max_leaf_nodes=35,max_features='log2',max_depth=35,criterion='entropy',random_state=42) #define classifiers\n",
    "extra_trees_clfF = ExtraTreesClassifier(n_estimators=71,max_leaf_nodes=45,max_features='log2',max_depth=35,criterion='gini', random_state=42)    \n",
    "svm_clfF = LinearSVC(penalty='l2', multi_class='ovr',max_iter=1075,loss='hinge',fit_intercept=True,dual=True,class_weight='balanced',C=1.3558155815581558, random_state=42)\n",
    "mlp_clfF = MLPClassifier(solver='lbfgs',max_iter=129,learning_rate='invscaling',hidden_layer_sizes=(100,41),early_stopping=True,alpha=10,activation='relu',random_state=42)\n",
    "softmax_reg_clfF = LogisticRegression(multi_class=\"multinomial\", solver=\"lbfgs\",C=10.0,dual=False,max_iter=53,penalty='l2', random_state=42)\n",
    "KNeighbors_clfF=KNeighborsClassifier(weights='distance',n_neighbors=15,leaf_size=1.0,algorithm='brute',n_jobs=-1)\n",
    "Perceptron_clfF=Perceptron(random_state=42,shuffle=True,penalty='l1',max_iter=1351,early_stopping=True,class_weight=None,alpha=0.01)\n",
    "AdaBoost_clfF=AdaBoostClassifier(base_estimator=RandomForestClassifier(random_state=42),n_estimators=175, learning_rate=3.5714285714285716 ,random_state=42,algorithm='SAMME')\n",
    "GradientBoosting_clfF=GradientBoostingClassifier(subsample=0.9,n_iter_no_change=None,n_estimators=300,max_leaf_nodes=4,max_features='sqrt',max_depth=36,loss='exponential',criterion='mae',random_state=42)\n",
    "Bagging_clfF=BaggingClassifier(n_estimators=84,base_estimator=RandomForestClassifier(random_state=42),bootstrap=True,random_state=42)\n",
    "tree_clfF=DecisionTreeClassifier(splitter='random',max_leaf_nodes=57,max_features=None,max_depth=6,criterion='entropy',random_state=42)\n",
    "log_reg_clfF = LogisticRegression(solver='liblinear',C=10.0,dual=True,max_iter=53,multi_class='auto',penalty='l2', random_state=42)\n",
    "SGDC_clfF=SGDClassifier(shuffle=False,penalty='l1',max_iter=1210,loss='hinge',learning_rate='adaptive',fit_intercept=True,eta0=3,early_stopping=False,alpha=0.01,random_state=42)\n",
    "GP_clfF=GaussianProcessClassifier(n_restarts_optimizer=1,multi_class='one_vs_rest',max_iter_predict=140,random_state=42)\n",
    "Non_linear_svm_clfF=SVC(kernel=\"rbf\",gamma='auto',degree=8,C=0.16931693169316933,probability=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the RandomForestClassifier(criterion='entropy', max_depth=35, max_features='log2',\n",
      "                       max_leaf_nodes=35, n_estimators=258, random_state=42)\n",
      "Training the ExtraTreesClassifier(max_depth=35, max_features='log2', max_leaf_nodes=45,\n",
      "                     n_estimators=71, random_state=42)\n",
      "Training the LinearSVC(C=1.3558155815581558, class_weight='balanced', loss='hinge',\n",
      "          max_iter=1075, random_state=42)\n",
      "Training the MLPClassifier(alpha=10, early_stopping=True, hidden_layer_sizes=(100, 41),\n",
      "              learning_rate='invscaling', max_iter=129, random_state=42,\n",
      "              solver='lbfgs')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:471: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the LogisticRegression(C=10.0, max_iter=53, multi_class='multinomial',\n",
      "                   random_state=42)\n",
      "Training the LogisticRegression(C=10.0, dual=True, max_iter=53, random_state=42,\n",
      "                   solver='liblinear')\n",
      "Training the KNeighborsClassifier(algorithm='brute', leaf_size=1.0, n_jobs=-1,\n",
      "                     n_neighbors=15, weights='distance')\n",
      "Training the Perceptron(alpha=0.01, early_stopping=True, max_iter=1351, penalty='l1',\n",
      "           random_state=42)\n",
      "Training the AdaBoostClassifier(algorithm='SAMME',\n",
      "                   base_estimator=RandomForestClassifier(random_state=42),\n",
      "                   learning_rate=3.5714285714285716, n_estimators=175,\n",
      "                   random_state=42)\n",
      "Training the GradientBoostingClassifier(criterion='mae', loss='exponential', max_depth=36,\n",
      "                           max_features='sqrt', max_leaf_nodes=4,\n",
      "                           n_estimators=300, random_state=42, subsample=0.9)\n",
      "Training the BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                  n_estimators=84, random_state=42)\n",
      "Training the DecisionTreeClassifier(criterion='entropy', max_depth=6, max_leaf_nodes=57,\n",
      "                       random_state=42, splitter='random')\n",
      "Training the SGDClassifier(alpha=0.01, eta0=3, learning_rate='adaptive', max_iter=1210,\n",
      "              penalty='l1', random_state=42, shuffle=False)\n",
      "Training the GaussianProcessClassifier(max_iter_predict=140, n_restarts_optimizer=1,\n",
      "                          random_state=42)\n",
      "Training the SVC(C=0.16931693169316933, degree=8, gamma='auto', probability=True,\n",
      "    random_state=42)\n"
     ]
    }
   ],
   "source": [
    "estimatorsF = [random_forest_clfF,\n",
    "              extra_trees_clfF,\n",
    "              svm_clfF, \n",
    "              mlp_clfF,\n",
    "              softmax_reg_clfF,\n",
    "              log_reg_clfF,\n",
    "              KNeighbors_clfF,\n",
    "              Perceptron_clfF, \n",
    "              AdaBoost_clfF, \n",
    "              GradientBoosting_clfF, \n",
    "              Bagging_clfF, \n",
    "              tree_clfF, \n",
    "              SGDC_clfF, \n",
    "              GP_clfF,\n",
    "              Non_linear_svm_clfF\n",
    "             ] #array of classifiers\n",
    "for estimatorF in estimatorsF:\n",
    "    print(\"Training the\", estimatorF)\n",
    "    estimatorF.fit(xtrain_tf, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RandomForestClassifier(criterion='entropy', max_depth=35, max_features='log2',\n",
      "                       max_leaf_nodes=35, n_estimators=258, random_state=42) cross validated scores on the train set are [0.92941176 0.8502994  0.89017341 0.88198758 0.85897436]\n",
      "F1-Score: 0.882 ± 0.028\n",
      "Accuracy: 0.868 ± 0.029\n",
      "\n",
      "The ExtraTreesClassifier(max_depth=35, max_features='log2', max_leaf_nodes=45,\n",
      "                     n_estimators=71, random_state=42) cross validated scores on the train set are [0.92397661 0.84848485 0.88757396 0.8875     0.83870968]\n",
      "F1-Score: 0.877 ± 0.031\n",
      "Accuracy: 0.864 ± 0.031\n",
      "\n",
      "The LinearSVC(C=1.3558155815581558, class_weight='balanced', loss='hinge',\n",
      "          max_iter=1075, random_state=42) cross validated scores on the train set are [0.92215569 0.82716049 0.88757396 0.8961039  0.83870968]\n",
      "F1-Score: 0.874 ± 0.036\n",
      "Accuracy: 0.862 ± 0.038\n",
      "\n",
      "The MLPClassifier(alpha=10, early_stopping=True, hidden_layer_sizes=(100, 41),\n",
      "              learning_rate='invscaling', max_iter=129, random_state=42,\n",
      "              solver='lbfgs') cross validated scores on the train set are [0.92857143 0.83950617 0.87719298 0.87654321 0.84210526]\n",
      "F1-Score: 0.873 ± 0.032\n",
      "Accuracy: 0.860 ± 0.033\n",
      "\n",
      "The LogisticRegression(C=10.0, max_iter=53, multi_class='multinomial',\n",
      "                   random_state=42) cross validated scores on the train set are [0.9112426  0.82716049 0.87209302 0.89171975 0.84076433]\n",
      "F1-Score: 0.869 ± 0.031\n",
      "Accuracy: 0.854 ± 0.033\n",
      "\n",
      "The LogisticRegression(C=10.0, dual=True, max_iter=53, random_state=42,\n",
      "                   solver='liblinear') cross validated scores on the train set are [0.9112426  0.83435583 0.88095238 0.89171975 0.84076433]\n",
      "F1-Score: 0.872 ± 0.030\n",
      "Accuracy: 0.858 ± 0.031\n",
      "\n",
      "The KNeighborsClassifier(algorithm='brute', leaf_size=1.0, n_jobs=-1,\n",
      "                     n_neighbors=15, weights='distance') cross validated scores on the train set are [0.91566265 0.85542169 0.88622754 0.8875     0.83221477]\n",
      "F1-Score: 0.875 ± 0.029\n",
      "Accuracy: 0.864 ± 0.028\n",
      "\n",
      "The Perceptron(alpha=0.01, early_stopping=True, max_iter=1351, penalty='l1',\n",
      "           random_state=42) cross validated scores on the train set are [0.9068323  0.84090909 0.87951807 0.85       0.8       ]\n",
      "F1-Score: 0.855 ± 0.036\n",
      "Accuracy: 0.835 ± 0.045\n",
      "\n",
      "The AdaBoostClassifier(algorithm='SAMME',\n",
      "                   base_estimator=RandomForestClassifier(random_state=42),\n",
      "                   learning_rate=3.5714285714285716, n_estimators=175,\n",
      "                   random_state=42) cross validated scores on the train set are [0.93413174 0.82208589 0.88888889 0.88198758 0.83870968]\n",
      "F1-Score: 0.873 ± 0.040\n",
      "Accuracy: 0.860 ± 0.042\n",
      "\n",
      "The GradientBoostingClassifier(criterion='mae', loss='exponential', max_depth=36,\n",
      "                           max_features='sqrt', max_leaf_nodes=4,\n",
      "                           n_estimators=300, random_state=42, subsample=0.9) cross validated scores on the train set are [0.92397661 0.8452381  0.90697674 0.88050314 0.8625    ]\n",
      "F1-Score: 0.884 ± 0.029\n",
      "Accuracy: 0.869 ± 0.031\n",
      "\n",
      "The BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                  n_estimators=84, random_state=42) cross validated scores on the train set are [0.93491124 0.84848485 0.88372093 0.88198758 0.8516129 ]\n",
      "F1-Score: 0.880 ± 0.031\n",
      "Accuracy: 0.866 ± 0.033\n",
      "\n",
      "The DecisionTreeClassifier(criterion='entropy', max_depth=6, max_leaf_nodes=57,\n",
      "                       random_state=42, splitter='random') cross validated scores on the train set are [0.89940828 0.85889571 0.86390533 0.87179487 0.8375    ]\n",
      "F1-Score: 0.866 ± 0.020\n",
      "Accuracy: 0.851 ± 0.021\n",
      "\n",
      "The SGDClassifier(alpha=0.01, eta0=3, learning_rate='adaptive', max_iter=1210,\n",
      "              penalty='l1', random_state=42, shuffle=False) cross validated scores on the train set are [0.92307692 0.82926829 0.88095238 0.87898089 0.84810127]\n",
      "F1-Score: 0.872 ± 0.032\n",
      "Accuracy: 0.858 ± 0.034\n",
      "\n",
      "The GaussianProcessClassifier(max_iter_predict=140, n_restarts_optimizer=1,\n",
      "                          random_state=42) cross validated scores on the train set are [0.91666667 0.82208589 0.84662577 0.85534591 0.79470199]\n",
      "F1-Score: 0.847 ± 0.041\n",
      "Accuracy: 0.834 ± 0.041\n",
      "\n",
      "The SVC(C=0.16931693169316933, degree=8, gamma='auto', probability=True,\n",
      "    random_state=42) cross validated scores on the train set are [0.9112426  0.86390533 0.88       0.88198758 0.85      ]\n",
      "F1-Score: 0.877 ± 0.021\n",
      "Accuracy: 0.861 ± 0.022\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for estimatorF in estimatorsF: #cross validation of all individual classifiers\n",
    "    score = cross_val_score(estimatorF, xtrain_tf, ytrain, scoring='f1', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "    print(\"The \"+str(estimatorF)+\" cross validated scores on the train set are \"+str(score))\n",
    "    print('F1-Score: %.3f ± %.3f' % (np.mean(score), np.std(score)))\n",
    "    score = cross_val_score(estimatorF, xtrain_tf, ytrain, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "    print('Accuracy: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will see how the classifiers perform on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report of  RandomForestClassifier(criterion='entropy', max_depth=35, max_features='log2',\n",
      "                       max_leaf_nodes=35, n_estimators=258, random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  ExtraTreesClassifier(max_depth=35, max_features='log2', max_leaf_nodes=45,\n",
      "                     n_estimators=71, random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  LinearSVC(C=1.3558155815581558, class_weight='balanced', loss='hinge',\n",
      "          max_iter=1075, random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  MLPClassifier(alpha=10, early_stopping=True, hidden_layer_sizes=(100, 41),\n",
      "              learning_rate='invscaling', max_iter=129, random_state=42,\n",
      "              solver='lbfgs')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  LogisticRegression(C=10.0, max_iter=53, multi_class='multinomial',\n",
      "                   random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  LogisticRegression(C=10.0, dual=True, max_iter=53, random_state=42,\n",
      "                   solver='liblinear')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  KNeighborsClassifier(algorithm='brute', leaf_size=1.0, n_jobs=-1,\n",
      "                     n_neighbors=15, weights='distance')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  Perceptron(alpha=0.01, early_stopping=True, max_iter=1351, penalty='l1',\n",
      "           random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  AdaBoostClassifier(algorithm='SAMME',\n",
      "                   base_estimator=RandomForestClassifier(random_state=42),\n",
      "                   learning_rate=3.5714285714285716, n_estimators=175,\n",
      "                   random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  GradientBoostingClassifier(criterion='mae', loss='exponential', max_depth=36,\n",
      "                           max_features='sqrt', max_leaf_nodes=4,\n",
      "                           n_estimators=300, random_state=42, subsample=0.9)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                  n_estimators=84, random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  DecisionTreeClassifier(criterion='entropy', max_depth=6, max_leaf_nodes=57,\n",
      "                       random_state=42, splitter='random')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  SGDClassifier(alpha=0.01, eta0=3, learning_rate='adaptive', max_iter=1210,\n",
      "              penalty='l1', random_state=42, shuffle=False)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  GaussianProcessClassifier(max_iter_predict=140, n_restarts_optimizer=1,\n",
      "                          random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n",
      "Classification report of  SVC(C=0.16931693169316933, degree=8, gamma='auto', probability=True,\n",
      "    random_state=42)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        77\n",
      "           1       0.93      0.85      0.89       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.87      0.88      0.87       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for estimatorF in estimatorsF:#classification reports\n",
    "    print(\"Classification report of \", estimatorF)\n",
    "    print(classification_report(ytest,estimator.predict(xtest_tf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RandomForestClassifier(criterion='entropy', max_depth=35, max_features='log2',\n",
      "                       max_leaf_nodes=35, n_estimators=258, random_state=42) f1-score on the test set is:\n",
      "0.901\n",
      "The ExtraTreesClassifier(max_depth=35, max_features='log2', max_leaf_nodes=45,\n",
      "                     n_estimators=71, random_state=42) f1-score on the test set is:\n",
      "0.900\n",
      "The LinearSVC(C=1.3558155815581558, class_weight='balanced', loss='hinge',\n",
      "          max_iter=1075, random_state=42) f1-score on the test set is:\n",
      "0.863\n",
      "The MLPClassifier(alpha=10, early_stopping=True, hidden_layer_sizes=(100, 41),\n",
      "              learning_rate='invscaling', max_iter=129, random_state=42,\n",
      "              solver='lbfgs') f1-score on the test set is:\n",
      "0.890\n",
      "The LogisticRegression(C=10.0, max_iter=53, multi_class='multinomial',\n",
      "                   random_state=42) f1-score on the test set is:\n",
      "0.874\n",
      "The LogisticRegression(C=10.0, dual=True, max_iter=53, random_state=42,\n",
      "                   solver='liblinear') f1-score on the test set is:\n",
      "0.874\n",
      "The KNeighborsClassifier(algorithm='brute', leaf_size=1.0, n_jobs=-1,\n",
      "                     n_neighbors=15, weights='distance') f1-score on the test set is:\n",
      "0.853\n",
      "The Perceptron(alpha=0.01, early_stopping=True, max_iter=1351, penalty='l1',\n",
      "           random_state=42) f1-score on the test set is:\n",
      "0.822\n",
      "The AdaBoostClassifier(algorithm='SAMME',\n",
      "                   base_estimator=RandomForestClassifier(random_state=42),\n",
      "                   learning_rate=3.5714285714285716, n_estimators=175,\n",
      "                   random_state=42) f1-score on the test set is:\n",
      "0.894\n",
      "The GradientBoostingClassifier(criterion='mae', loss='exponential', max_depth=36,\n",
      "                           max_features='sqrt', max_leaf_nodes=4,\n",
      "                           n_estimators=300, random_state=42, subsample=0.9) f1-score on the test set is:\n",
      "0.895\n",
      "The BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                  n_estimators=84, random_state=42) f1-score on the test set is:\n",
      "0.909\n",
      "The DecisionTreeClassifier(criterion='entropy', max_depth=6, max_leaf_nodes=57,\n",
      "                       random_state=42, splitter='random') f1-score on the test set is:\n",
      "0.874\n",
      "The SGDClassifier(alpha=0.01, eta0=3, learning_rate='adaptive', max_iter=1210,\n",
      "              penalty='l1', random_state=42, shuffle=False) f1-score on the test set is:\n",
      "0.864\n",
      "The GaussianProcessClassifier(max_iter_predict=140, n_restarts_optimizer=1,\n",
      "                          random_state=42) f1-score on the test set is:\n",
      "0.878\n",
      "The SVC(C=0.16931693169316933, degree=8, gamma='auto', probability=True,\n",
      "    random_state=42) f1-score on the test set is:\n",
      "0.889\n"
     ]
    }
   ],
   "source": [
    "for estimatorF in estimatorsF:\n",
    "    f1_test=f1_score(ytest,estimatorF.predict(xtest_tf))\n",
    "    print(\"The \"+str(estimatorF)+\" f1-score on the test set is:\")\n",
    "    print(\"%.3f\" % f1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrices\n",
    "fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15,20))\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "for estimatorF, ax in zip(estimatorsF, axes.flatten()):\n",
    "    plot_confusion_matrix(estimatorF, \n",
    "                          xtest_tf, \n",
    "                          ytest, \n",
    "                          ax=ax, \n",
    "                          cmap='cividis',\n",
    "                          normalize='true'\n",
    "                         )\n",
    "    ax.title.set_text(type(estimatorF).__name__)\n",
    "                          \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.3 Fine-Tuned Voting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the above models performed well (considering that we have not yet fine-tuned any hyperparameter) others...not that great.<br>What we will try to do now, is create a Voting classifier consisting of our best models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hard Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "named_estimatorsF = [\n",
    "    (\"random_forest_clfF\", random_forest_clfF),\n",
    "    (\"extra_trees_clfF\", extra_trees_clfF),\n",
    "    #(\"svm_clfF\",svm_clfF),\n",
    "    #(\"mlp_clfF\", mlp_clfF),\n",
    "    #(\"softmax_reg_clfF\",softmax_reg_clfF),\n",
    "    #(\"log_reg_clfF\", log_reg_clfF),\n",
    "    #(\"KNeighbors_clfF\", KNeighbors_clfF),\n",
    "    #(\"Perceptron_clfF\", Perceptron_clfF),\n",
    "    (\"AdaBoost_clfF\", AdaBoost_clfF),\n",
    "    (\"GradientBoosting_clfF\", GradientBoosting_clfF),\n",
    "    (\"Bagging_clfF\", Bagging_clfF),\n",
    "    #(\"tree_clfF\",tree_clfF),\n",
    "    #(\"SGDC_clfF\", SGDC_clfF),\n",
    "    #(\"GP_clfF\", GP_clfF),\n",
    "    (\"Non_linear_svm_clfF\", Non_linear_svm_clfF)\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clfF',\n",
       "                              RandomForestClassifier(criterion='entropy',\n",
       "                                                     max_depth=35,\n",
       "                                                     max_features='log2',\n",
       "                                                     max_leaf_nodes=35,\n",
       "                                                     n_estimators=258,\n",
       "                                                     random_state=42)),\n",
       "                             ('extra_trees_clfF',\n",
       "                              ExtraTreesClassifier(max_depth=35,\n",
       "                                                   max_features='log2',\n",
       "                                                   max_leaf_nodes=45,\n",
       "                                                   n_estimators=71,\n",
       "                                                   random_state=42)),\n",
       "                             ('AdaBoost_clfF',\n",
       "                              AdaBoostClassifier(algorith...\n",
       "                                                         loss='exponential',\n",
       "                                                         max_depth=36,\n",
       "                                                         max_features='sqrt',\n",
       "                                                         max_leaf_nodes=4,\n",
       "                                                         n_estimators=300,\n",
       "                                                         random_state=42,\n",
       "                                                         subsample=0.9)),\n",
       "                             ('Bagging_clfF',\n",
       "                              BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
       "                                                n_estimators=84,\n",
       "                                                random_state=42)),\n",
       "                             ('Non_linear_svm_clfF',\n",
       "                              SVC(C=0.16931693169316933, degree=8, gamma='auto',\n",
       "                                  probability=True, random_state=42))],\n",
       "                 n_jobs=-1)"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clfF = VotingClassifier(named_estimatorsF,n_jobs=-1) #Training the voting classifier\n",
    "voting_clfF.fit(xtrain_tf, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Hard Voting classifier cross validated f1-score on the train set are [0.92941176 0.84848485 0.9005848  0.8875     0.8516129 ]\n",
      "F1-Score: 0.884 ± 0.031\n",
      "\n",
      "The Hard Voting classifier cross validated accuracy-score on the train set are [0.91836735 0.82993197 0.88435374 0.87755102 0.84246575]\n",
      "Accuracy: 0.871 ± 0.032\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#cross validation of Hard votin clf\n",
    "score = cross_val_score(voting_clfF, xtrain_tf, ytrain, scoring='f1', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "print(\"The Hard Voting classifier cross validated f1-score on the train set are \"+str(score))\n",
    "print('F1-Score: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")\n",
    "\n",
    "score = cross_val_score(voting_clfF, xtrain_tf, ytrain, scoring='accuracy', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "print(\"The Hard Voting classifier cross validated accuracy-score on the train set are \"+str(score))\n",
    "print('Accuracy: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Soft Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_estimators_softF = [\n",
    "    (\"random_forest_clfF\", random_forest_clfF),\n",
    "    (\"extra_trees_clfF\", extra_trees_clfF),\n",
    "    #(\"svm_clfF\",svm_clfF),\n",
    "    #(\"mlp_clfF\", mlp_clfF),\n",
    "    #(\"softmax_reg_clfF\",softmax_reg_clfF),\n",
    "    #(\"log_reg_clfF\", log_reg_clfF),\n",
    "    #(\"KNeighbors_clfF\", KNeighbors_clfF),\n",
    "    #(\"Perceptron_clfF\", Perceptron_clfF),\n",
    "    #(\"AdaBoost_clfF\", AdaBoost_clfF),\n",
    "    (\"GradientBoosting_clfF\", GradientBoosting_clfF),\n",
    "    (\"Bagging_clfF\", Bagging_clfF),\n",
    "    #(\"tree_clfF\",tree_clfF),\n",
    "    #(\"SGDC_clfF\", SGDC_clfF),\n",
    "    #(\"GP_clfF\", GP_clfF),\n",
    "    (\"Non_linear_svm_clfF\", Non_linear_svm_clfF)\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clfF',\n",
       "                              RandomForestClassifier(criterion='entropy',\n",
       "                                                     max_depth=35,\n",
       "                                                     max_features='log2',\n",
       "                                                     max_leaf_nodes=35,\n",
       "                                                     n_estimators=258,\n",
       "                                                     random_state=42)),\n",
       "                             ('extra_trees_clfF',\n",
       "                              ExtraTreesClassifier(max_depth=35,\n",
       "                                                   max_features='log2',\n",
       "                                                   max_leaf_nodes=45,\n",
       "                                                   n_estimators=71,\n",
       "                                                   random_state=42)),\n",
       "                             ('GradientBoosting_clfF',\n",
       "                              GradientBoostingCla...\n",
       "                                                         loss='exponential',\n",
       "                                                         max_depth=36,\n",
       "                                                         max_features='sqrt',\n",
       "                                                         max_leaf_nodes=4,\n",
       "                                                         n_estimators=300,\n",
       "                                                         random_state=42,\n",
       "                                                         subsample=0.9)),\n",
       "                             ('Bagging_clfF',\n",
       "                              BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
       "                                                n_estimators=84,\n",
       "                                                random_state=42)),\n",
       "                             ('Non_linear_svm_clfF',\n",
       "                              SVC(C=0.16931693169316933, degree=8, gamma='auto',\n",
       "                                  probability=True, random_state=42))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf_softF = VotingClassifier(named_estimators_softF, voting='soft') #Training the softvoting classifier\n",
    "voting_clf_softF.fit(xtrain_tf, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Soft Voting classifier cross validated f1-scores on the train set are [0.92397661 0.84848485 0.89655172 0.875      0.84810127]\n",
      "F1-Score: 0.878 ± 0.029\n",
      "\n",
      "The Soft Voting classifier cross validated accuracy-scores on the train set are [0.91156463 0.82993197 0.87755102 0.86394558 0.83561644]\n",
      "Accuracy: 0.864 ± 0.030\n",
      "\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(voting_clf_softF, xtrain_tf, ytrain, scoring='f1', cv=cv, n_jobs=-1) #f1: the harmonic mean of sensitivity and precision\n",
    "print(\"The Soft Voting classifier cross validated f1-scores on the train set are \"+str(score))\n",
    "print('F1-Score: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")\n",
    "\n",
    "score = cross_val_score(voting_clf_softF, xtrain_tf, ytrain, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "print(\"The Soft Voting classifier cross validated accuracy-scores on the train set are \"+str(score))\n",
    "print('Accuracy: %.3f ± %.3f' % (np.mean(score), np.std(score))+\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets try them with the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report of  VotingClassifier(estimators=[('random_forest_clfF',\n",
      "                              RandomForestClassifier(criterion='entropy',\n",
      "                                                     max_depth=35,\n",
      "                                                     max_features='log2',\n",
      "                                                     max_leaf_nodes=35,\n",
      "                                                     n_estimators=258,\n",
      "                                                     random_state=42)),\n",
      "                             ('extra_trees_clfF',\n",
      "                              ExtraTreesClassifier(max_depth=35,\n",
      "                                                   max_features='log2',\n",
      "                                                   max_leaf_nodes=45,\n",
      "                                                   n_estimators=71,\n",
      "                                                   random_state=42)),\n",
      "                             ('AdaBoost_clfF',\n",
      "                              AdaBoostClassifier(algorith...\n",
      "                                                         loss='exponential',\n",
      "                                                         max_depth=36,\n",
      "                                                         max_features='sqrt',\n",
      "                                                         max_leaf_nodes=4,\n",
      "                                                         n_estimators=300,\n",
      "                                                         random_state=42,\n",
      "                                                         subsample=0.9)),\n",
      "                             ('Bagging_clfF',\n",
      "                              BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                                                n_estimators=84,\n",
      "                                                random_state=42)),\n",
      "                             ('Non_linear_svm_clfF',\n",
      "                              SVC(C=0.16931693169316933, degree=8, gamma='auto',\n",
      "                                  probability=True, random_state=42))],\n",
      "                 n_jobs=-1)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.88      0.87        77\n",
      "           1       0.91      0.90      0.91       107\n",
      "\n",
      "    accuracy                           0.89       184\n",
      "   macro avg       0.89      0.89      0.89       184\n",
      "weighted avg       0.89      0.89      0.89       184\n",
      "\n",
      "Classification report of  VotingClassifier(estimators=[('random_forest_clfF',\n",
      "                              RandomForestClassifier(criterion='entropy',\n",
      "                                                     max_depth=35,\n",
      "                                                     max_features='log2',\n",
      "                                                     max_leaf_nodes=35,\n",
      "                                                     n_estimators=258,\n",
      "                                                     random_state=42)),\n",
      "                             ('extra_trees_clfF',\n",
      "                              ExtraTreesClassifier(max_depth=35,\n",
      "                                                   max_features='log2',\n",
      "                                                   max_leaf_nodes=45,\n",
      "                                                   n_estimators=71,\n",
      "                                                   random_state=42)),\n",
      "                             ('GradientBoosting_clfF',\n",
      "                              GradientBoostingCla...\n",
      "                                                         loss='exponential',\n",
      "                                                         max_depth=36,\n",
      "                                                         max_features='sqrt',\n",
      "                                                         max_leaf_nodes=4,\n",
      "                                                         n_estimators=300,\n",
      "                                                         random_state=42,\n",
      "                                                         subsample=0.9)),\n",
      "                             ('Bagging_clfF',\n",
      "                              BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                                                n_estimators=84,\n",
      "                                                random_state=42)),\n",
      "                             ('Non_linear_svm_clfF',\n",
      "                              SVC(C=0.16931693169316933, degree=8, gamma='auto',\n",
      "                                  probability=True, random_state=42))],\n",
      "                 voting='soft')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.87      0.86        77\n",
      "           1       0.90      0.89      0.90       107\n",
      "\n",
      "    accuracy                           0.88       184\n",
      "   macro avg       0.88      0.88      0.88       184\n",
      "weighted avg       0.88      0.88      0.88       184\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification report of \", voting_clfF)\n",
    "print(classification_report(ytest,voting_clfF.predict(xtest_tf)))\n",
    "print(\"Classification report of \", voting_clf_softF)\n",
    "print(classification_report(ytest,voting_clf_softF.predict(xtest_tf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The VotingClassifier(estimators=[('random_forest_clfF',\n",
      "                              RandomForestClassifier(criterion='entropy',\n",
      "                                                     max_depth=35,\n",
      "                                                     max_features='log2',\n",
      "                                                     max_leaf_nodes=35,\n",
      "                                                     n_estimators=258,\n",
      "                                                     random_state=42)),\n",
      "                             ('extra_trees_clfF',\n",
      "                              ExtraTreesClassifier(max_depth=35,\n",
      "                                                   max_features='log2',\n",
      "                                                   max_leaf_nodes=45,\n",
      "                                                   n_estimators=71,\n",
      "                                                   random_state=42)),\n",
      "                             ('AdaBoost_clfF',\n",
      "                              AdaBoostClassifier(algorith...\n",
      "                                                         loss='exponential',\n",
      "                                                         max_depth=36,\n",
      "                                                         max_features='sqrt',\n",
      "                                                         max_leaf_nodes=4,\n",
      "                                                         n_estimators=300,\n",
      "                                                         random_state=42,\n",
      "                                                         subsample=0.9)),\n",
      "                             ('Bagging_clfF',\n",
      "                              BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                                                n_estimators=84,\n",
      "                                                random_state=42)),\n",
      "                             ('Non_linear_svm_clfF',\n",
      "                              SVC(C=0.16931693169316933, degree=8, gamma='auto',\n",
      "                                  probability=True, random_state=42))],\n",
      "                 n_jobs=-1) f1-score on the test set is:\n",
      "0.906\n",
      "The VotingClassifier(estimators=[('random_forest_clfF',\n",
      "                              RandomForestClassifier(criterion='entropy',\n",
      "                                                     max_depth=35,\n",
      "                                                     max_features='log2',\n",
      "                                                     max_leaf_nodes=35,\n",
      "                                                     n_estimators=258,\n",
      "                                                     random_state=42)),\n",
      "                             ('extra_trees_clfF',\n",
      "                              ExtraTreesClassifier(max_depth=35,\n",
      "                                                   max_features='log2',\n",
      "                                                   max_leaf_nodes=45,\n",
      "                                                   n_estimators=71,\n",
      "                                                   random_state=42)),\n",
      "                             ('GradientBoosting_clfF',\n",
      "                              GradientBoostingCla...\n",
      "                                                         loss='exponential',\n",
      "                                                         max_depth=36,\n",
      "                                                         max_features='sqrt',\n",
      "                                                         max_leaf_nodes=4,\n",
      "                                                         n_estimators=300,\n",
      "                                                         random_state=42,\n",
      "                                                         subsample=0.9)),\n",
      "                             ('Bagging_clfF',\n",
      "                              BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42),\n",
      "                                                n_estimators=84,\n",
      "                                                random_state=42)),\n",
      "                             ('Non_linear_svm_clfF',\n",
      "                              SVC(C=0.16931693169316933, degree=8, gamma='auto',\n",
      "                                  probability=True, random_state=42))],\n",
      "                 voting='soft') f1-score on the test set is:\n",
      "0.896\n"
     ]
    }
   ],
   "source": [
    "votersF=[voting_clfF, voting_clf_softF]\n",
    "for voterF in votersF:\n",
    "    f1_test=f1_score(ytest,voterF.predict(xtest_tf))\n",
    "    print(\"The \"+str(voterF)+\" f1-score on the test set is:\")\n",
    "    print(\"%.3f\" % f1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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45uUrH/FedV5mjHppR9UjlVK3mBtG1tcHb/nsTL7/n/dw9WmLOfOn07n2pinrbXPSuZuw6w5rufIrS7johKW8+6TNWL0GHje/jytOWcIVpyzhsi8tYZONkgP2faCmb6Ju6ulJPv/2e3jJu+aw4LCtefV+97PrDoNkhs89MjM8uBr2e9sc9nrd1uz1uq154dMe4Gm7eYSjG6rIDRExAzgI+GBmrsjMS4DzgEPbfN/pI31Gt49wtlU9T2aXXT+Vx2y3lp227WPaVHjV81dx3q+nr7dNBCy/v4dMWLEqmLVZP73NvmVtX7DqwWDtWrj/gWDb2f01fAvV4am7ruYvt/fyt7/3smZtcNZPN2b/fVett82ie6dw+fXTWLN2/ffeuWQKf1g4DYAVq3q47qZetpvd162mT1oVTqmtdKRS6iJzwwguu34qj9m2ryU3PMD3h80NPevlhnUuvnIaO23bxw5bmxsmg0ZmmNqSGTYZIjNsxJq+gUVLsHJVo4SY2ptM7U1P+eiSMeaG2RFxecty1IDd7gL0ZebClnVXASP91h4ELAJ+OVK7u1ZwjrV6nmxuXzSF7ec8HPS3m9PH7YvW7xX+9cCVXH9zL/MOmsseR8zhM29eRk8PbDenn3e+agU7/stctjtoLptv2s8/PsURp8liuzl93Hb3w38rty2aMqaicYet17LXLmv43bXTOtk8DaLUkUqpG8wN7bljcQ/zthqQGxavH++OOfB+rr9lCvNfMYe9Xrcln/635fQMSIBnXTydV+3n0c3JYrvZg2SGOe1nhp6e5Mqv3Mld37uDiy6fzmXXbVRFMzXAGHPD4szcu2U5ecBuNwUGzoteBmzG8F4LfC1z5OGGbh7hHGv1PKkM9l8sYv21P7psI/Z47Bpu++5dXHnKIt7y2c25b2Vwz/LgvF9P569n3s1t372LlauCb/zYKbWTxWClyGgHHGds3M93jl/C2z+3Bcvv9xTvquUYFsbBSKXUJeaGNgwW9WJAh/Dj309jj8eu5ZbvLOLyU5bw1s9txn0rH95o9Ro4/zfTecVzLDgni4F/IzC6C9P19wdPev3WbP+KbXnK41ez4NGrO9c4DWmMuWEkK4CZA9bNBIY8xSYitgeeA3ytnQ/oZqJsu3qOiKPWhalFS9cOfHlCmzenj1tbjmjevmjKI6bFnvbDTTjw2Q8QAY+d18ejt+nj+lt6ueiKjdhxm7XM2aKfqb1w4LMf4NJrPEo1Wdy2aMp6o9zz5vRxx+Ipw7xjfb1Tku8cv4Rv/WQTzvmlAxVVS0Y/SjleRiqlLhlTblg8yXLDdnP61ztSdfuiKWy75fq54fQLN+bAZzVzw3Z97NjMDev88Hcbsdcua5g7y+m0k8WGZoZ1lq3o4Rd/3IgXPc3BiqptQG4YyUKgNyJ2blm3B3DNMO85DPhNZt7Yzgd0s+Bsu3rOzJPXhak5sybXrUKf8rg1/OW2Xv729ymsXgNnXbwx/7zP+v8nnr9VHxdf0Zi6cNfSHm64tZedtulj/lZ9/O7aadz/QJAJF1+5EbvuMLk63sns99dPY+d5a9lxm7VM7U1etd8qzruk3cIxOeU993D9TVP5zFkj1SXqiOoub175SKXUJWPKDbMnW254/Br+cvuUltwwnZfus/7pNNvP7efiKx/ODQtv7WWnbR/OB2ddPJ1XPd+CYTJpZIY1LZnhfs77dXuZYfbmfWy+aWNwYvq0fvZ78gNcf/PUKpsrqCw3ZOZK4GzguIiYERHPBPYHvj7M2w4DTmu36d38VX6oes7M/22uG6l6nnR6e+Fzb13Gi9+1JX39cMSL72fBo9fyxe81LlP+pv3v5wOHLeeI/9yCPY6YQyZ87Kj7mL1FP7O36Oeg5zzA3m+YTe8U2HPnNbzhpStr/kbqlr6+4M2f2YIffmoxU3qSr14wg2tvmsob918BwJe+tylzZ/Xx+y/fzcwZ/fT3w1tfuYIFh87liY9Zw2Evup+r/zqVK0+9C4D3nzyTC3/rkc4qVXQscSy/taMaqZS6xNzQht4p8Nm33Mc/vftR9PXD4S9exYJHr+VL5zV+v9/4slW8/9AVvP7jm7Pn67aEhI8etZzZmzd+gO5/AC66YiO+8I5H3hZDE1dfX/DmEx7FDz+5qJEZfrBpIzO8rJkZzmtmhpPvejgzvGIFCw7bmm227OO09y1lyhToieR/frYJF1xqXuiGCucgHQOcCtwNLAGOzsxrImI+cC2wW2beAhARzwDmMYqLDEY3Z09FxJk0phMfCewJ/ADYJzOH7Dz2fuKMvOyC3brUQk0UU/bbse4mqES3/JR8YGl79x7ZQLs8Zm7+93++etTve9G/fO6KzNx7uG1G+1sbETcAH8/MU0fdIKlCY8kNT37ijPzd+bt3qYWaKKb+ww51N0EluvmiCZEbqtbtq4IcA2xMo3o+g2b13OU2SNK4UOFtUQb9rY2I+c37bc5ft+FYRiqlLjI3SFJThbmhUl090SEzlwIHdPMzJWmyGeq3tjkdZtMB6y4FZnSpadKomBskqXyT68x6SRonGpcr78osHEmSVLiSc4MFpyTVZLxMdZEkSeNfqbnBglOS6jCOzq2QJEnjXMG5wYJTkmrS5g2ZJUmSis0NFpySVIME+gsdqZQkSd1Vcm6w4JSkmpR68r8kSeq+UnODBack1aTUczEkSVL3lZobLDglqSaldhySJKn7Ss0NFpySVIeMYk/+lyRJXVZwbuipuwGSJEmSpInJI5ySVIOk3KkxkiSpu0rODRacklSTUjsOSZLUfaXmBgtOSapJqZc3lyRJ3VdqbrDglKSalDpSKUmSuq/U3GDBKUl1yHI7DkmS1GUF5wYLTkmqQckn/0uSpO4qOTdYcEpSTUq9n5YkSeq+UnODBack1aTQgUpJklSDUnODBack1aTUqTGSJKn7Ss0NPXU3QJIkSZI0MXmEU5JqUupIpSRJ6r5Sc4MFpyTVILPck/8lSVJ3lZwbLDglqSaFDlRKkqQalJobLDglqSalTo2RJEndV2pusOCUpJqU2nFIkqTuKzU3eJVaSapB0jgXY7SLJEmafKrMDRExKyLOiYiVEXFzRBw8zLY7RcT5EbE8IhZHxCdG2r8FpyTVpHEBgNEt7ai645AkSd1XVW4ATgRWA3OBQ4CTImLBwI0iYhrwE+BiYGtgHvCNkXbulFpJqkmFM2NaO449gQsi4qrMvKZ1o5aO40TgVUAfsEt1zZIkSWNVRW6IiBnAQcDumbkCuCQizgMOBd4zYPPDgTsy89Mt664e6TM8wilJdRjDKGU7I5UtHccHM3NFZl4CrOs4BjqcZseRmSsz84HMHLHjkCRJXTb23DA7Ii5vWY4asOddgL7MXNiy7irgEUc4gacDN0XEhc1ZUT+PiCeM1HSPcEpSWWZHxOUtz0/OzJNbng/VcTxnkH091HEATwH+DLw5M//U6UZLkqRaLM7MvYd5fVNg2YB1y4DNBtl2HvA84GXAT4G3At+LiMdn5uqhPsCCU5JqMsarzdXecUiSpO6r6Cq1K4CZA9bNBJYPsu0q4JLMvBAgIj4JfADYlcbg9qCcUitJNUhGf6W5Nq82N6aOo1lgfhLYkkbHIUmSxokKc8NCoDcidm5ZtwdwzSDbXs0YTiW14JSkmuQYljZU3nFIkqTuqyI3ZOZK4GzguIiYERHPBPYHvj7I5t8Anh4RL4iIKcDbgMXAdcN9xpBTaiPi5W20cV1Dz253W0lSQxVTYzJzZUSs6ziOpHGV2v2BfQbZ/BvAOyPiBcDPgLfQRschDcbcIEnVqmhKLcAxwKnA3cAS4OjMvCYi5gPXArtl5i2ZeUNEvAb4IrAVcCXwspFOwxnuHM7vtNnABKa0ua0kqanUjkMagrlBkipUVW7IzKXAAYOsv4XGtSFa151N44ho24YsODPT6baSVJXR3ZB5dLuuuOOQBmNukKQKVZgbquZVaiWpBkm5HYckSequknND26OREfHiiLggIq6LiO2b646MiP2qa54kTVxJjHqRSmFukKTOKjU3tFVwRsQhwLdpXP1wR2Bq86UpwLsraZkkTXCZo1+kEpgbJKnzSs0N7R7hfDfwhsx8O7C2Zf1vaVwBUZIkaR1zgyQJaP8czp2BSwdZP9gNxiVJ7RgnI49SBcwNktRpheaGdo9w3gHsMsj6ZwN/7VxzJGnyKHVqjNQGc4MkdVipuaHdgvNk4HMR8czm8+0j4rXAJ4CTKmmZJE1wOYZFKoS5QZI6rNTc0NaU2sz8RERsDvwEmA78DHgQ+GRmnlhh+yRpQhpPI49Sp5kbJKmzSs4Nbd+HMzPfHxH/AexG48jotZm5orKWSdIEV2rHIbXD3CBJnVVqbmi74GxK4IHm474Ot0WSJpVSOw5pFMwNktQhpeaGdu/DuVFEnAAsBa4CrgaWRsRnI2J6lQ2UpImq1HMxpJGYGySp80rNDe0e4TwJ+EfgSB6+zPkzgI8BmwGv63zTJGliK3WkUmqDuUGSOqzU3NBuwflK4OWZ+ZOWdTdGxN3Ad7HjkKRRScrtOKQ2mBskqYNKzg3t3hZlJXD7IOtvB1Z1rjmSJGkCMDdIkoD2C87/Bj4UERuvW9F8/MHma5Kk0RjDzZtLHdnUpGRukKROKjg3DDmlNiLOG7DqucDtEXF18/kTmu+fUU3TJGliGyf9gNQR5gZJqlapuWG4cziXDHj+3QHP/9bhtkjSpDJeRh6lDjE3SFKFSs0NQxacmXlENxsiSZNNZtTdBKljzA2SVK1Sc0O7V6mVJHVQyVebkyRJ3VVybmi74IyII4D/A8wHprW+lpk7dbhdkjThFdpvSG0xN0hSZ5WaG9q6Sm1EvAv4FHAFsCNwLvBnYBZwalWNk6SJrNSrzUkjMTdIUueVmhvavS3KG4CjMvO9wBrg85n5MhqdyQ5VNU6SJqyCL28utcHcIEmdVHBuaLfgnAdc1ny8CpjZfHwGcFCnGyVJk0FmjnqRCmFukKQOKzU3tFtw3gnMbj6+GXhG8/FjKXc6sSRJqoa5QZIEtF9wXgy8rPn4K8CnI+JnwFnA2VU0TJImshzjIhXC3CBJHVRybmi34DwKOB4gM78IHA78CXg/cEwlLZOkCa6qczEiYlZEnBMRKyPi5og4eIjtDo+IvohY0bI8t4NfUZOXuUGSOqzU3NDWbVEysx/ob3l+Fo1RSknSGFV4asWJwGpgLrAncEFEXJWZ1wyy7aWZuW9lLdGkZG6QpM4rNTcMWXBGxJPa3UlmXjmaD5UkUclcl4iYQeOiLLtn5grgkog4DzgUeE/nP1FqMDdIUsUKzQ3DHeG8nMbXihH2kcCUTjRmMFfcMJUpz92mqt1rgur79a11N0EFeuoLV3f18yoaqNwF6MvMhS3rrgKeM8T2e0XEYmAp8HXgY5m5tpqmaYIbF7nhyhumMvV5c6vavSaovl/fUncTVCBzQ3u5YbiC89GjaakkaRTGfn+s2RFxecvzkzPz5JbnmwLLBrxnGbDZIPv6JbA7jauILqAx5XEt8LExtUyTnblBkqpScG4YsuDMzJuHe6MkaeySMXccizNz72FeX8HD9zxcZyaw/BFtyLyx5emfIuI44F1YcGoMzA2SVJ2Sc0O7V6mVJHVYRZc3Xwj0RsTOLev2AAY78X+wJo00HVKSJNWg1NxgwSlJNcnMUS9t7HMljfscHhcRMyLimcD+NM6zWE9EvDgi5jYfPx74IPC9jn5JSZLUEaXmBgtOSZp4jgE2Bu4GzgCOzsxrImJ+855Z85vb7QdcHRErgR/Q6HA+WkuLJUlSXSrNDW3dh1OS1HlV3U8rM5cCBwyy/hYaFwdY9/xY4NhqWiFJkjqp1NwwqiOcETE7Ip4WERuN9oMkSevLHP0ilcTcIEmdU2puaKvgjIjNIuLbNA6z/gbYrrn+ixHx4eqaJ0kT1VhO/R8nPYc0AnODJHVaubmh3SOcH6fRWTwJWNWy/nzgwE43SpImurGMUo6XkUqpDeYGSeqgknNDu+dwvgw4MDP/GBGtTb8O2KnzzZKkia+dq8dJhTI3SFKHlZob2i04HwUsGWT9ZkBf55ojSZNHof2G1A5zgyR1WKm5od0ptb+nMVq5zrqv+0Ya52ZIkkapzDMxpLaYGySpw0rNDe0e4Xwf8KOIWD5wRKsAABo1SURBVNB8zzuaj58KPLuqxknSRFbq1BipDeYGSeqwUnNDW0c4M/M3wD7ANOCvNG76eQfwjMy8srrmSdIEVupQpTQCc4MkVaDQ3NDuEU4y80/AaytsiyRJmiDMDZIkaLPgjIhZw72emUs70xxJmhzG0cCj1HHmBknqrJJzQ7tHOBcz/Hec0oG2SNLkkeWeiyG1wdwgSZ1UcG5ot+B83oDnU4G9gKOBD3S0RZI0SRTab0jtMDdIUoeVmhvaKjgz8xeDrL4oIm4EjgS+1dFWSdIkUGrHIY3E3CBJnVdqbmj7okFD+CNe3lySxiSLPRtDGjNzgySNUam5YcwFZ0RsCrwNuLVzzZGkySEpd6RSGgtzgySNXcm5od2r1C5n/ZP/A9gEWAkcUkG7JGniK7TjkEZibpCkChSaG9o9wvlvA573A4uA32XmPZ1tkiRNDoX2G1I7zA2S1GGl5oYRC86I6AVmAOdm5h3VN0mSJoHMYi9vLg3H3CBJFSg4N/SMtEFmrgX+i8YlzSVJkoZkbpAktRqx4Gz6LfDkKhsiSZNN5ugXqRDmBknqsFJzQ7vncH4Z+GREzAeuoHHS/0My88pON0ySJrrx0hFIFTA3SFKHlZobhi04I+JUGpcwX3eD5k8PslkCUzrcLkma0JJyT/6XhmJukKRqlJwbRjrC+VrgPcCju9AWSZpUSj35XxqGuUGSKlJqbhip4AyAzLy5C22RpEml0H5DGo65QZIqUmpuaOeiQYV+NUmSVANzgyTpIe1cNOjOiBh2g8z0XAxJGo1xdPU4qcPMDZLUaQXnhnYKzqOAe6tuiCRNNlV1HBExC/gK8I/AYuC9mfmtEd5zMfA8YGrzPorSWJkbJKkCE7ng/H5m3l15SyRpEkkqPfn/RGA1MBfYE7ggIq7KzGsG2zgiDqH922RJIzE3SFKHVZkbqh6oHukczkLraEmanCJiBnAQ8MHMXJGZlwDnAYcOsf3mwIeAd3evlZrAzA2SVJ7WgepDgJMiYsFQG492oHqkgnP4kzAkSWOWY1iA2RFxecty1IDd7gL0ZebClnVXAUN1HB8FTgLu7MBXkswNklSRMeaGYXVjoHrYyjQz27mKrSRpDMY4M2ZxZu49zOubAssGrFsGbDZww4jYG3gm8FZg3phaI7UwN0hSdcaYG2ZHxOUtz0/OzJNbng81UP2cIfY36oFqz9mRpJpUdCrGCmDmgHUzgeWtKyKiB/gC8NbMXDvSVUUlSVK9Sh2odiRSkmpSxdQYYCHQGxE7t6zbAxh4waCZwN7AWRFxJ/D75vrbIuJZo/82kiSpShXlhjENVI+m3R7hlKQ6NC431/ndZq6MiLOB4yLiSBpXqd0f2GfApsuAbVuebw9cBjwZWNTxhkmSpLGrKDfQMlCdmf/bXDfSQDXAuvsp3xYRr8zMXw31ARacklSD6voNAI4BTgXuBpYAR2fmNRExH7gW2C0zb6Hl/IuImN58eJf34ZQkaXypKjd0Y6DaglOSalJVvZmZS4EDBll/C41zNQZ7z014hVFJksatCu87VelAtQWnJNWkwiOckiRpgqkqN1Q9UG3BKUk1seCUJEntKjU3eJVaSZIkSVIlPMIpSTUpdaRSkiR1X6m5wYJTkmrQuD9WoT2HJEnqqpJzgwWnJNUhyx2plCRJXVZwbrDglKSalNpxSJKk7is1N1hwSlJNCu03JElSDUrNDRacklSXUnsOSZLUfYXmBgtOSapJqVNjJElS95WaGyw4JakGSbEDlZIkqctKzg0WnJJUk1JHKiVJUveVmht66m6AJEmSJGli8ginJNWh4PtpSZKkLis4N1hwSlItkiy155AkSV1Wbm6w4JSkGpR88r8kSequknODBack1aTQgUpJklSDUnODBack1aTUjkOSJHVfqbnBglOSalJovyFJkmpQam6w4JSkmpQ6UilJkrqv1NxgwSlJNciCL28uSZK6q+TcYMEpSTUptN+QJEk1KDU39NTdAEmSJEnSxOQRTkmqSalTYyRJUveVmhs8wlmAFz5tNdedsZSF317Cvx96/yNef9wOa/n1yfew6ueLeOf/eeTrmpx++Ntp7PrqLdnlX7bk41/f5BGv33Nf8PL3bs6eh83i6UfO4s83TqmhlZPbuvMxRrNI0kAbkhO+8r7l3HnBYq7+xtJuNVfjxEg5YdmK4GXv3oK9XjuLJxyyJV+9YPpDr33u2xvzxNdsyRMO2ZLPnvXI96oapeaGrhacEfFvEXF5RDwYEad187NL1dOTfP7Y5bzknZuz4OBZvPoFD7DrjmvX22bpfT289TOb8qkz/D+8Gvr64M2f2owLPnUvf/7mEs68aDrX/m39gvJjX5vBnjuv5Y9fW8ppH1zG20/YrKbWTl45hkWaLMwM7dnQnHDaDzbixW/fvFvN1TjRTk74wnc3Zrcd1/KH05dy8eeX8q7/3ozVa+DPN07hlPM24benLOEPpy/hgt9M439vddC6G0rNDd0+wnkHcDxwapc/t1hP3W0tf7ltCn+7Ywpr1gZnXTSd/Z+1er1tFt3Tw+XXTWXN2iF2oknnsuum8ph5fey0XR/TpsKr9nuA83610XrbXHtTL89/cuNv6fE79HHT36dw11InPXRLUt1IZUTMiohzImJlRNwcEQcPsd2rI+KGiFgWEXdHxOkRMbODX1PaEGaGNmxoTvjVH6ex9D5/+yebdnJCBCy/P8iEFauCWTP76Z0C193Uy9MWrGGT6dDbC8/ecw3n/nKjIT5JnVJlbqhaV39hMvPszDwXWNLNzy3ZdnP6ue2uh0eNblvUw3Zz+mpskUpw+6Iett+q/6Hn223Vz+2L1h993OOxaznnF40O4rJre7n5rincdreho2vG0GmMouM4EVgNzAUOAU6KiAWDbPdr4JmZuTmwE43z+o/f8C8nbTgzQ3vMCRqLdnLCvx60iutv6mXe/rPZ47At+czbltPTA7vvtJZfXTWVJcuC+x+ACy+dxq13eYSzctXmhkqZLse5GGTdePnj0fg12N9IDPhj+vdDV3LP8h6e9NpZfP47m7DXzmvptb/oqio6joiYARwEfDAzV2TmJcB5wKGP/Py8NTMXt6zqAx7bmW8nqRvMCRqLdnLCjy6bxh47r+G27y3mytOW8pZPz+S+lcGuO/bxrkNW8sK3PYqXvONRPPGxa+md4h9dN5Q6M2pcFpwRcVTzvI3L6V898hsmsNsW9TBv7sMjlfPm9HPHYqsCDW/eVv3c2nK08va7e9h29voj3jNnJKe+/z6uPH0pp3/wPhbd28Ojt3VUvJvGeC7G7HW/j83lqAG73QXoy8yFLeuuAgY7wklE7BsRy4DlNArVEzry5aQumsy5wZygsWgnJ5x2wcYc+JwHiYDHzuvj0dv0cf3Njb+t1//zA1z+1aX8/Av3MGtmsvP25oduqPAczkpnRo3LgjMzT87MvTNzb3qm1d2cWv3+ul52ntfHjtv0MbU3edULHuC8Syb3v4lG9pTHr2me09PD6jVw1k+n88/7PrjeNvcuD1avaTw+5fsb86w9VzNzhiOU3TTGkcrF634fm8vJA3a7KbBswLplwKBXhcrMS5odxzzgv4CbOvolpS6YzLnBnKCxaCcnzJ/bx8VXNP6W7lraww23TGGn5sD03fc0DofecmcP5/xiI179gge6+wUmqVJnRnkfznGury9486c35YefWcaUKclXz5/OtX/r5Y0HrALgS+duzNxZ/fz+1HuYOSPp74e3vmoVCw5+FMvvH5fjCeqC3l743NuX8+J3PIq+PjjipQ+wYKc+vnjOxgC86cBVXHdzL4f/v5lM6YFdd1zLKe+9r+ZWTy7rTv6vwApg4PSWmTSOYA7dnszbI+KHwJnAkyppmaSO29Cc8M2P3Mdz91rD7C36ueXcJXz4lE049fyNa/5Wqlo7OeEDh6/kiP+YyR6HziIz+NgxK5i9RaPjeuX7tmDJfT1M7U3++53LedRMB6yrtgG5YXZEXN7y/OQBg9VDzYx6zmA7i4h9gQtoZIv7gQNHakBXC86I6G1+5hRgSkRMB9ZmptdXHcaFl27EhZeuf/WvL537cGdw19Ie5h+wZbebpXHuJfus5iX7rH+tjTcduOqhx8/YfQ03nOW1OCaghUBvROycmf/bXLcHcE0b7+0FHlNZy6RRMDO0b0NywiEf8sLUk9VIOWHbOf386IR7B33vL066p9K2qaMWZ+bew7w+6plRwOYRsR3wBtqYGdXtQ2AfAFYB7wFe03z8gS63QZLGhSrOxcjMlcDZwHERMSMingnsD3x94LYRcUhEzI+GHYD/AH66wV9M6gwzgyS1qOgczjHPjALWzYwaVrdvi/LhzIwBy4e72QZJGi8qvLz5McDGwN3AGcDRmXlNs7hcERHzm9vtBvyGRmfza+AGGqOVUu3MDJK0vopyw0Mzo1rWdXRmlOdwSlIdRldAjm7XmUuBAwZZfwuNqTPrnr8feH81rZAkSR1TUW7IzJURsW5m1JHAnjRmRu0zcNuIOAT4FXArMJ82Z0Z5VRlJqkmFlzeXJEkTTIW5odKZUR7hlKQaVHiVWkmSNMFUmRuqnhllwSlJNbHglCRJ7So1N1hwSlJNCu03JElSDUrNDRacklSTUkcqJUlS95WaGyw4JakGCfQX2nFIkqTuKjk3eJVaSZIkSVIlPMIpSXWo8D6ckiRpgik4N1hwSlJNCu03JElSDUrNDRacklSLIDPqboQkSSpCubnBglOSalLq1BhJktR9peYGC05JqkFS7tQYSZLUXSXnBgtOSapJqZc3lyRJ3VdqbrDglKSalDo1RpIkdV+pucGCU5JqUmi/IUmSalBqbrDglKQaZJY7NUaSJHVXybmhp+4GSJIkSZImJo9wSlJNSj0XQ5IkdV+pucGCU5JqkpR5A2dJktR9peYGC05JqkFS7rkYkiSpu0rODRacklSTUqfGSJKk7is1N1hwSlJNSh2plCRJ3VdqbrDglKSaFNpvSJKkGpSaGyw4JakGJd9PS5IkdVfJucGCU5Jqklnm1eYkSVL3lZobLDglqSb9dTdAkiQVo9Tc0FN3AyRJnRURsyLinIhYGRE3R8TBQ2z32oi4IiLui4jbIuITEeFApCRJ6hgLTkmqwbr7aY12adOJwGpgLnAIcFJELBhku02AtwGzgacB+wHHbuh3kyRJnVVxbqiUBack1aSKjiMiZgAHAR/MzBWZeQlwHnDowG0z86TM/FVmrs7M24FvAs/s7LeUJEmdUFXBWfXMKAtOSarBBoxUzo6Iy1uWowbsehegLzMXtqy7ChjsCOdAzwau6cT3kyRJnVPyzCjP1ZGkmvQzpqvNLc7MvYd5fVNg2YB1y4DNhttpRBwB7A0cOZZGSZKkao0xNwyrZWbU7pm5ArgkItbNjHpP67aZeVLL09sj4pvA80b6DAtOSapDdedWrABmDlg3E1g+1Bsi4gDgP4EXZObiSlolSZLGrrrcMNTMqOe08d62ZkZZcEpSDdZNjanAQqA3InbOzP9trtuDITqEiHgR8GXgnzLzT5W0SJIkbZANyA2zI+LylucnZ+bJLc8rnxllwSlJNemrYJ+ZuTIizgaOi4gjgT2B/YF9Bm4bEc+ncaGgAzPzsgqaI0mSOmSMuWGkU3EqnxnlRYMkqQYJ9OXolzYdA2wM3A2cARydmddExPyIWBER85vbfRDYHPhBc/2KiLiww19VkiRtoApzw0Mzo1rWtTMz6p/bnRnlEU5JqkECayu6P1ZmLgUOGGT9LTSmzqx7PuKJ/pIkqX5V5YZuzIzyCKckSZIkTV6VzozyCKck1aSvgsubS5Kkiamq3FD1zCgLTkmqQZVTaiVJ0sRScm6w4JSkOiRkoR2HJEnqsoJzgwWnJNWm0J5DkiTVoMzcYMEpSXUps9+QJEl1KDQ3WHBKUm0K7TkkSVINyswNFpySVJfsr7sFkiSpFIXmBgtOSapFwWf/S5KkLis3N1hwSlJtyhyplCRJdSgzN/TU3QBJkiRJ0sTkEU5Jqkuh52JIkqQaFJobLDglqQ6ZxXYckiSpywrODRacklSbMjsOSZJUhzJzgwWnJNWi3JFKSZLUbeXmBgtOSapLoZc3lyRJNSg0N1hwSlJtyhyplCRJdSgzN1hwSlItyp0aI0mSuq3c3GDBKUl1KbTjkCRJNSg0N1hwSlJtyuw4JElSHcrMDT11N0CSJEmSNDF5hFOS6pBZ7NXmJElSlxWcGyw4JakuhZ6LIUmSalBobrDglKS6FNpxSJKkGhSaGyw4JakW2VwkSZJGUm5usOCUpLoUOlIpSZJqUGhusOCUpFqUewNnSZLUbeXmBgtOSapDUuzV5iRJUpcVnBsix3nDI2IRcHPd7RinZgOL626EiuPfzdB2yMw53figiPghjf8Wo7U4M1/U6fZIE4W5YVj+/mss/LsZmrmhDeO+4NTQIuLyzNy77naoLP7dSNLk5O+/xsK/G22onrobIEmSJEmamCw4JUmSJEmVsOAs28l1N0BF8u9GkiYnf/81Fv7daIN4DqckSZIkqRIe4ZQkSZIkVcKCU5IkSZJUCQtOSZIkSVIlLDgLFBGzIuKciFgZETdHxMF1t0njX0T8W0RcHhEPRsRpdbdHktQd5gaNhblBndJbdwM0JicCq4G5wJ7ABRFxVWZeU2+zNM7dARwPvBDYuOa2SJK6x9ygsTA3qCO8Sm1hImIGcA+we2YubK77OnB7Zr6n1sapCBFxPDAvMw+vuy2SpGqZG7ShzA3aUE6pLc8uQN+6TqPpKmBBTe2RJEnjl7lBUq0sOMuzKbBswLplwGY1tEWSJI1v5gZJtbLgLM8KYOaAdTOB5TW0RZIkjW/mBkm1suAsz0KgNyJ2blm3B+CJ/5IkaSBzg6RaWXAWJjNXAmcDx0XEjIh4JrA/8PV6W6bxLiJ6I2I6MAWYEhHTI8IrVUvSBGZu0FiZG9QpFpxlOobG5anvBs4AjvbS5mrDB4BVwHuA1zQff6DWFkmSusHcoLEwN6gjvC2KJEmSJKkSHuGUJEmSJFXCglOSJEmSVAkLTkmSJElSJSw4JUmSJEmVsOCUJEmSJFXCglOSJEmSVAkLTtUiIl4REdny/PCIWFFTW86PiNOGef25EZERMXsU+/x5RHx+A9u1Y/Nz996Q/UiSVDpzQ1v7MDdoXLLg1EMi4rTmD1VGxJqIuDEiPhkRM7rw8WcBO7W7cUTcFBHHVtgeSZI0DHODpHb01t0AjTsXAYcCU4FnAacAM4CjB24YEb1AX2bmwNdGKzNXAas2dD+SJKmrzA2ShuURTg30YGbemZm3Zua3gG8CBwBExIcj4s/NaSx/BR4EZkTE5hFxckTcHRHLI+IXA6dzRMRhEXFzRNwfEecDcwe8/oipMRHxTxHxu4hYFRFLIuL7ETE9In4O7AD817qR1Zb37NP8/Psj4vaIOCkiZra8vklzRHZFRNwVEe8b7T9QRGwZEWdExG3Ntl0TEUcMsmlvRHw2Iu5pLv8VET0t+5kWER9v7mdlRPw+Il442vZIklQjc8MIzA2a7Cw4NZJVNEYt13k0cDDwSmAPGp3HBcB2wEuBvYBfAhdHxDYAEfE04DTgZGBP4PvAccN9aES8CPge8BPgycDzgF/Q+Jt9OXBbcx/bNBci4gnAj4Hzmm17efPzTm3Z9SeBfwAOAvZrtvfZo/j3AJgOXNn8vguAzwJfioj9Bmx3SLO9zwDeCBwFvK3l9a8Cz6Hx7/kE4HTg+xGxxyjbI0nSeGFueCRzgya3zHRxITOh8eN+fsvzpwKLgbOazz8MrAHmtmzzfGAFsPGAff0ReHfz8beAnwx4/ZTGn99Dzw8HVrQ8/zVw5jBtvQk4dsC6rwFfGbBuTyCBrYBNaXR0h7S8vilwL3DaMJ/13OY+Zg+zzZnAKS3Pfw4sBKJl3QeA25qPHwP0A/MH7Odc4AvNxzs2P3fvuv82XFxcXFxcBi7mhiE/y9zg4tKyeA6nBnpRc4pKL40Ryu8Bb255/bbMvKvl+ZOBTYBFEdG6n+k0fhwBdqUxOtnqUuD1w7RjLxod2Wg8GXhsRLyqZd26Rj0GuB+Y1vxsADJzRUT8aTQfEhFTgPcAr6IxQrtRc78/H7DpbzOz9TyVS4H/15yq86Rm264d8O+2EXDxaNojSVKNzA0jMDdosrPg1EC/pDGFYw1wR2auGfD6ygHPe4C7aFwoYKD7mv8bg7xWhR4aI6CfGeS124HHdehzjgXeCbwV+BONkdqP0hgNbVcPjVHIp9D4t27lRRAkSaUwN4zM3KBJzYJTA92fmX8ZxfZX0jiRvz8zbxxim2uBpw9YN/D5QH+gca7El4d4fTUwZZC2LBiq/RHxFxo/0k8HbmyumwHsDvx1hPa02hf4fmZ+vbmPAHahMcWm1dMiIlpGK59OozO+LyL+QKND3TozfzaKz5YkaTwxN4zM3KBJzYsGaUNdROO8ie9FxIsj4tER8YyI+EhErBu9/Bzwgoh4b0TsHBFvAA4cYb//AbwyIo6PiN0iYkFEvD0iNmm+fhPwrIjYLh6+sfLHgadGxBcjYq+IeGxEvDQivgSNaTDAV4CPR8Q/RMQCGhcGGNgBjWQhsF9E7BsRjwc+T+OiCANtC5wQEY+LiFcA76I5ipqZC2lcye+0aNzMeqeI2Dsijo2Il4+yPZIklcLcYG7QJGPBqQ3SHIV7CY3zB74M3AB8m8Y0lDua2/yWxnkXRwNX07gK3IdH2O8PaHQuL6YxavkLGlec629u8n+B7WmMMC5qvudqGleO27G5/VXAx2hM3VnnWOBnwDnN//0zjelAo3E8cBlwYfO9K2l0AgN9k0an9Dsa/zZfYf1pO0fQuOLcJ4DrgfOb7b95lO2RJKkI5gZzgyafWP/cZEmSJEmSOsMjnJIkSZKkSlhwSpIkSZIqYcEpSZIkSaqEBackSZIkqRIWnJIkSZKkSlhwSpIkSZIqYcEpSZIkSaqEBackSZIkqRL/H7FMjY3gXQehAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,5))\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "for voterF, ax in zip(votersF, axes.flatten()):\n",
    "    plot_confusion_matrix(voterF, \n",
    "                          xtest_tf, \n",
    "                          ytest, \n",
    "                          ax=ax, \n",
    "                          cmap='cividis',\n",
    "                          normalize='true'\n",
    "                         )\n",
    "    ax.title.set_text(type(voterF).__name__)\n",
    "                          \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we managed to train various classifiers to predict if a patient\n",
    "is at high risk of suffering from hear disease, based on 11 clinical and personal\n",
    "features.<br>\n",
    "The models we trained, in general, managed to do well in the test set with an\n",
    "average accuracy, for our good models, of 90%. Notably, the tree based ensembles, seem to perform particularly good in the dataset as it can be seen on the\n",
    "relative confusion matrices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE END!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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