pred-5_tres-classes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quinta abordagem de predição: 3 classes\n",
    "\n",
    "CLASSE 0 = notas 0, 1, 2 e 3 (negativo)\n",
    "\n",
    "CLASSE 1 = notas 4, 5, 6, 7 e 8 (confuso)\n",
    "\n",
    "CLASSE 2 = notas 9 e 10 (positivo)\n",
    "\n",
    "- com equilíbrio de amostras\n",
    "- com validação cruzada \n",
    "- com otimização de parâmetros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas e Configurações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importação dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANF</th>\n",
       "      <th>USERVALUEQ1_LABEL</th>\n",
       "      <th>USERVALUEQ2_VALUE</th>\n",
       "      <th>vol_Rede_Social</th>\n",
       "      <th>vol_Video</th>\n",
       "      <th>vol_Comunicacao</th>\n",
       "      <th>vol_Loja_de_Apps</th>\n",
       "      <th>vol_Musica</th>\n",
       "      <th>vol_Google</th>\n",
       "      <th>vol_Navegacao</th>\n",
       "      <th>...</th>\n",
       "      <th>term_fabr_motorola</th>\n",
       "      <th>term_fabr_não informado</th>\n",
       "      <th>term_fabr_outros</th>\n",
       "      <th>term_fabr_samsung</th>\n",
       "      <th>term_fabr_xiaomi</th>\n",
       "      <th>device_tech_2G</th>\n",
       "      <th>device_tech_3G</th>\n",
       "      <th>device_tech_4G</th>\n",
       "      <th>device_tech_5G</th>\n",
       "      <th>Qtd_CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>Não Informado</td>\n",
       "      <td>8.111028</td>\n",
       "      <td>6.282269</td>\n",
       "      <td>5.894406</td>\n",
       "      <td>5.347112</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.295874</td>\n",
       "      <td>3.295874</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Plano/Bonus</td>\n",
       "      <td>7.180832</td>\n",
       "      <td>5.153297</td>\n",
       "      <td>5.283209</td>\n",
       "      <td>1.946053</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>Velocidade</td>\n",
       "      <td>8.094379</td>\n",
       "      <td>5.846442</td>\n",
       "      <td>6.682110</td>\n",
       "      <td>2.890427</td>\n",
       "      <td>4.844195</td>\n",
       "      <td>1.609638</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81</td>\n",
       "      <td>10</td>\n",
       "      <td>Outros</td>\n",
       "      <td>4.897847</td>\n",
       "      <td>5.771444</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>10</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>3.784212</td>\n",
       "      <td>3.434019</td>\n",
       "      <td>5.918897</td>\n",
       "      <td>1.791926</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <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>45201</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>5.262695</td>\n",
       "      <td>5.398167</td>\n",
       "      <td>3.828663</td>\n",
       "      <td>5.075180</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45202</th>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>7.978654</td>\n",
       "      <td>6.054442</td>\n",
       "      <td>7.756196</td>\n",
       "      <td>3.912043</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.401231</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45203</th>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>Duracao pacote dados</td>\n",
       "      <td>3.434019</td>\n",
       "      <td>5.332724</td>\n",
       "      <td>4.248510</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45204</th>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>5.159061</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45205</th>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>Duracao pacote dados</td>\n",
       "      <td>2.639129</td>\n",
       "      <td>3.637612</td>\n",
       "      <td>2.890427</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45206 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ANF  USERVALUEQ1_LABEL     USERVALUEQ2_VALUE  vol_Rede_Social  \\\n",
       "0       51                  8         Não Informado         8.111028   \n",
       "1       11                  9           Plano/Bonus         7.180832   \n",
       "2       37                 10            Velocidade         8.094379   \n",
       "3       81                 10                Outros         4.897847   \n",
       "4       85                 10       Sinal/Cobertura         3.784212   \n",
       "...    ...                ...                   ...              ...   \n",
       "45201   48                  0       Sinal/Cobertura         5.262695   \n",
       "45202   35                  7       Sinal/Cobertura         7.978654   \n",
       "45203   43                 10  Duracao pacote dados         3.434019   \n",
       "45204   42                 10       Sinal/Cobertura        -6.907755   \n",
       "45205   22                  9  Duracao pacote dados         2.639129   \n",
       "\n",
       "       vol_Video  vol_Comunicacao  vol_Loja_de_Apps  vol_Musica  vol_Google  \\\n",
       "0       6.282269         5.894406          5.347112   -6.907755    3.295874   \n",
       "1       5.153297         5.283209          1.946053   -6.907755   -6.907755   \n",
       "2       5.846442         6.682110          2.890427    4.844195    1.609638   \n",
       "3       5.771444         1.386544         -6.907755   -6.907755    0.001000   \n",
       "4       3.434019         5.918897          1.791926   -6.907755    1.386544   \n",
       "...          ...              ...               ...         ...         ...   \n",
       "45201   5.398167         3.828663          5.075180   -6.907755    0.001000   \n",
       "45202   6.054442         7.756196          3.912043   -6.907755    3.401231   \n",
       "45203   5.332724         4.248510          1.386544   -6.907755   -6.907755   \n",
       "45204   5.159061         1.386544         -6.907755   -6.907755   -6.907755   \n",
       "45205   3.637612         2.890427         -6.907755   -6.907755   -6.907755   \n",
       "\n",
       "       vol_Navegacao  ...  term_fabr_motorola  term_fabr_não informado  \\\n",
       "0           3.295874  ...                   0                        0   \n",
       "1          -6.907755  ...                   0                        0   \n",
       "2          -6.907755  ...                   1                        0   \n",
       "3          -6.907755  ...                   0                        0   \n",
       "4          -6.907755  ...                   0                        1   \n",
       "...              ...  ...                 ...                      ...   \n",
       "45201      -6.907755  ...                   0                        0   \n",
       "45202      -6.907755  ...                   0                        0   \n",
       "45203      -6.907755  ...                   1                        0   \n",
       "45204      -6.907755  ...                   0                        0   \n",
       "45205      -6.907755  ...                   1                        0   \n",
       "\n",
       "       term_fabr_outros  term_fabr_samsung  term_fabr_xiaomi  device_tech_2G  \\\n",
       "0                     0                  0                 0               0   \n",
       "1                     0                  1                 0               0   \n",
       "2                     0                  0                 0               0   \n",
       "3                     1                  0                 0               0   \n",
       "4                     0                  0                 0               0   \n",
       "...                 ...                ...               ...             ...   \n",
       "45201                 0                  0                 1               0   \n",
       "45202                 0                  1                 0               0   \n",
       "45203                 0                  0                 0               0   \n",
       "45204                 0                  1                 0               0   \n",
       "45205                 0                  0                 0               0   \n",
       "\n",
       "       device_tech_3G  device_tech_4G  device_tech_5G  Qtd_CRM  \n",
       "0                   0               1               0        0  \n",
       "1                   0               1               0        0  \n",
       "2                   0               1               0        0  \n",
       "3                   1               0               0        0  \n",
       "4                   0               1               0        0  \n",
       "...               ...             ...             ...      ...  \n",
       "45201               0               1               0        0  \n",
       "45202               0               1               0        0  \n",
       "45203               0               0               1        0  \n",
       "45204               1               0               0        0  \n",
       "45205               0               1               0        0  \n",
       "\n",
       "[45206 rows x 62 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('NPS_Complete_UFF_2022-07-18_2022-12-31_extra-tratado.csv', sep=';')\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notas atribuídas (distribuição)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    18937\n",
       "8      5477\n",
       "9      4578\n",
       "0      4099\n",
       "7      2879\n",
       "5      2756\n",
       "6      1684\n",
       "1      1438\n",
       "3      1202\n",
       "2      1147\n",
       "4      1009\n",
       "Name: USERVALUEQ1_LABEL, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['USERVALUEQ1_LABEL'].value_counts() # divisão de clientes por nota atribuída"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint, uniform\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split -> 3 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    23515\n",
       "1    13805\n",
       "0     7886\n",
       "Name: CLASSE, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CLASSE 0: Notas de 0 a 3\n",
    "# CLASSE 1: Notas de 4 a 8\n",
    "# CLASSE 2: Notas 9 e 10\n",
    "\n",
    "df['CLASSE'] = df['USERVALUEQ1_LABEL'].apply(lambda x: 0 if x <= 3 else 1 if x <= 8 else 2)\n",
    "\n",
    "df['CLASSE'].value_counts() # distribuição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Igualar a quantidade de amostras de cada classe fazendo 7800 de cada\n",
    "df = df.groupby('CLASSE').apply(lambda x: x.sample(7800)).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Amostrar 70% para treino e 30% para teste\n",
    "df_train = df.sample(frac=0.7, random_state=1)\n",
    "df_test = df.drop(df_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separar os dados de treino em X e y exluindo dados que vazam informação\n",
    "X_train = df_train.drop(columns=['USERVALUEQ1_LABEL', 'USERVALUEQ2_VALUE', 'NPS_Class_Detrator', 'NPS_Class_Passivo', 'NPS_Class_Promotor', 'CLASSE'])\n",
    "y_train = df_train['CLASSE']\n",
    "\n",
    "# Separar os dados de teste em X e y\n",
    "X_test = df_test.drop(columns=['USERVALUEQ1_LABEL', 'USERVALUEQ2_VALUE', 'NPS_Class_Detrator', 'NPS_Class_Passivo', 'NPS_Class_Promotor', 'CLASSE'])\n",
    "y_test = df_test['CLASSE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'binary:hinge',\n",
       " 'use_label_encoder': None,\n",
       " 'base_score': None,\n",
       " 'booster': None,\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': None,\n",
       " 'colsample_bynode': None,\n",
       " 'colsample_bytree': None,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': None,\n",
       " 'gpu_id': None,\n",
       " 'grow_policy': None,\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': None,\n",
       " 'learning_rate': None,\n",
       " 'max_bin': None,\n",
       " 'max_cat_threshold': None,\n",
       " 'max_cat_to_onehot': None,\n",
       " 'max_delta_step': None,\n",
       " 'max_depth': None,\n",
       " 'max_leaves': None,\n",
       " 'min_child_weight': None,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': None,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'num_parallel_tree': None,\n",
       " 'predictor': None,\n",
       " 'random_state': None,\n",
       " 'reg_alpha': None,\n",
       " 'reg_lambda': None,\n",
       " 'sampling_method': None,\n",
       " 'scale_pos_weight': None,\n",
       " 'subsample': None,\n",
       " 'tree_method': None,\n",
       " 'validate_parameters': None,\n",
       " 'verbosity': None}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(objective='binary:hinge')\n",
    "xgb.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "hyper_dict = {\n",
    "    \"colsample_bytree\": uniform(0.7, 0.3), # percentual de colunas a serem amostradas para cada árvore (colunas)\n",
    "    \"learning_rate\": uniform(0.03, 0.3), # taxa de aprendizado \n",
    "    \"max_depth\": randint(4, 8), # profundidade máxima de cada árvore\n",
    "    \"n_estimators\": randint(100, 250), # número de árvores\n",
    "    \"subsample\": uniform(0.5, 0.9) # percentual de amostras a serem usadas para cada árvore (linhas)\n",
    "}\n",
    "\n",
    "search_cv = RandomizedSearchCV(xgb, param_distributions=hyper_dict, n_iter=10, scoring='f1', random_state=0)\n",
    "search_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "{'colsample_bytree': 0.8646440511781974, 'learning_rate': 0.24455680991172585, 'max_depth': 7, 'n_estimators': 203, 'subsample': 0.8812893194050142}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best params:\")\n",
    "print(search_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.8646440511781974, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.24455680991172585,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=203, n_jobs=None, num_parallel_tree=None,\n",
       "              objective=&#x27;multi:softprob&#x27;, predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.8646440511781974, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.24455680991172585,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=203, n_jobs=None, num_parallel_tree=None,\n",
       "              objective=&#x27;multi:softprob&#x27;, predictor=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.8646440511781974, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.24455680991172585,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=203, n_jobs=None, num_parallel_tree=None,\n",
       "              objective='multi:softprob', predictor=None, ...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_xgb = search_cv.best_estimator_\n",
    "best_model_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.40      0.41      2368\n",
      "           1       0.34      0.37      0.35      2282\n",
      "           2       0.41      0.40      0.40      2370\n",
      "\n",
      "    accuracy                           0.39      7020\n",
      "   macro avg       0.39      0.39      0.39      7020\n",
      "weighted avg       0.39      0.39      0.39      7020\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = best_model_xgb.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matriz de confusão\n",
    "cm = confusion_matrix(y_test, y_pred, normalize='true')\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(cm, annot=True, fmt=\".2f\", linewidths=0.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Real');\n",
    "plt.xlabel('Predito');\n",
    "all_sample_title = 'Acurácia: {:.2f}'.format(best_model_xgb.score(X_test, y_test))\n",
    "plt.title(all_sample_title, size = 15);\n",
    "plt.rcParams.update({'font.size': 20})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           importance\n",
      "Qtd_CRM                      0.038272\n",
      "CUSTOMER_SEGMENT_Pré-pago    0.029270\n",
      "CUSTOMER_SEGMENT_Controle    0.027060\n",
      "dias_franquia_zerada         0.022526\n",
      "term_fabr_outros             0.020693\n",
      "term_fabr_apple              0.020405\n",
      "dias_traf_red                0.019922\n",
      "term_fabr_não informado      0.019690\n",
      "FLAG_PARAMETRO_R             0.019168\n",
      "term_fabr_lg                 0.018622\n",
      "SURVEY_Base                  0.018619\n",
      "CUSTOMER_SEGMENT_Pós-pago    0.018278\n",
      "imsi_LTE                     0.018268\n",
      "ACD_STATUS_p                 0.017529\n",
      "SURVEY_Uso Dados             0.017471\n",
      "VoLTE_perc                   0.017381\n",
      "vol_MarketPlace              0.017279\n",
      "qtde_celula                  0.017245\n",
      "tot_volume                   0.017202\n",
      "vol_Musica                   0.017131\n",
      "DISP_STATUS_p                0.017124\n",
      "vol_Ecommerce                0.017036\n",
      "vol_Video                    0.017031\n",
      "tot_duracao_chamada          0.016953\n",
      "vol_Alimentacao              0.016817\n",
      "originada_perc               0.016524\n",
      "vol_Transporte               0.016502\n",
      "ANF                          0.016433\n",
      "vol_Jogos                    0.016386\n",
      "vol_4g_perc                  0.016349\n",
      "tot_sms_env_receb            0.016313\n",
      "vol_Viagem                   0.016290\n",
      "vol_Comunicacao              0.016275\n",
      "eficiencia_Voz               0.016190\n",
      "OCUP_STATUS_p                0.016129\n",
      "vol_Navegacao                0.016105\n",
      "term_fabr_asus               0.016101\n",
      "vol_Rede_Social              0.016043\n",
      "device_tech_3G               0.015988\n",
      "vol_gratuito_perc            0.015982\n",
      "vol_Loja_de_Apps             0.015979\n",
      "vol_InternetBank             0.015907\n",
      "eficiencia_pdp               0.015807\n",
      "term_fabr_motorola           0.015648\n",
      "tot_chamada                  0.015641\n",
      "qtde_municipio               0.015634\n",
      "vol_Google                   0.015634\n",
      "FLAG_EXISTENCIA_R            0.015501\n",
      "dias_consumo_bonus_ctrl      0.015471\n",
      "vol_TIM                      0.015390\n",
      "FLAG_ELEGIBILIDADE_R         0.015379\n",
      "vol_ul_perc                  0.015373\n",
      "term_fabr_samsung            0.015312\n",
      "term_fabr_xiaomi             0.015094\n",
      "device_tech_5G               0.014552\n",
      "device_tech_4G               0.014197\n",
      "device_tech_2G               0.012878\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "feature_importances = pd.DataFrame(best_model_xgb.feature_importances_,\n",
    "                                    index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma das features mais importantes\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.set_style('white')\n",
    "sns.set(font_scale=1.4)\n",
    "sns.barplot(x=feature_importances.index, y=feature_importances['importance'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 234, in __call__\n",
      "    return self._score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 282, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1146, in f1_score\n",
      "    return fbeta_score(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1287, in fbeta_score\n",
      "    _, _, f, _ = precision_recall_fscore_support(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1573, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1391, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_search.py:952: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan nan nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA359C0&gt;,\n",
       "                                        &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA363B0&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA349A0&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA352D0&gt;},\n",
       "                   random_state=0, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA359C0&gt;,\n",
       "                                        &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA363B0&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA349A0&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA352D0&gt;},\n",
       "                   random_state=0, scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
       "                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA359C0>,\n",
       "                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA363B0>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA349A0>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000240AAA352D0>},\n",
       "                   random_state=0, scoring='f1')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyper_dict = {\n",
    "    \"max_depth\": randint(4, 8), # profundidade máxima de cada árvore\n",
    "    \"n_estimators\": randint(100, 250), # número de árvores\n",
    "    \"min_samples_leaf\": randint(1, 4), # número mínimo de amostras em cada folha\n",
    "    \"max_features\": randint(2, 10) # número máximo de features a serem consideradas em cada árvore\n",
    "}\n",
    "\n",
    "search_cv = RandomizedSearchCV(rf, param_distributions=hyper_dict, n_iter=10, scoring='f1', random_state=0)\n",
    "search_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "{'max_depth': 4, 'max_features': 9, 'min_samples_leaf': 2, 'n_estimators': 167}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best params:\")\n",
    "print(search_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=4, max_features=9, min_samples_leaf=2,\n",
       "                       n_estimators=167)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=4, max_features=9, min_samples_leaf=2,\n",
       "                       n_estimators=167)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=4, max_features=9, min_samples_leaf=2,\n",
       "                       n_estimators=167)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_rf = search_cv.best_estimator_\n",
    "best_model_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.47      0.36      0.41      2368\n",
      "           1       0.37      0.42      0.39      2282\n",
      "           2       0.42      0.47      0.45      2370\n",
      "\n",
      "    accuracy                           0.41      7020\n",
      "   macro avg       0.42      0.41      0.41      7020\n",
      "weighted avg       0.42      0.41      0.41      7020\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = best_model_rf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matriz de confusão\n",
    "cm = confusion_matrix(y_test, y_pred, normalize='true')\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(cm, annot=True, fmt=\".2f\", linewidths=0.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Real');\n",
    "plt.xlabel('Predito');\n",
    "all_sample_title = 'Acurácia: {:.2f}'.format(best_model_rf.score(X_test, y_test))\n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           importance\n",
      "qtde_celula                  0.087445\n",
      "dias_franquia_zerada         0.087081\n",
      "CUSTOMER_SEGMENT_Pré-pago    0.084390\n",
      "Qtd_CRM                      0.082481\n",
      "VoLTE_perc                   0.047263\n",
      "vol_MarketPlace              0.044599\n",
      "ANF                          0.044550\n",
      "vol_Navegacao                0.044410\n",
      "vol_4g_perc                  0.037390\n",
      "CUSTOMER_SEGMENT_Controle    0.033422\n",
      "vol_Musica                   0.032923\n",
      "qtde_municipio               0.031466\n",
      "tot_volume                   0.031211\n",
      "tot_sms_env_receb            0.023504\n",
      "originada_perc               0.021710\n",
      "eficiencia_pdp               0.021468\n",
      "vol_TIM                      0.020550\n",
      "FLAG_EXISTENCIA_R            0.017166\n",
      "vol_Rede_Social              0.017114\n",
      "tot_chamada                  0.016766\n",
      "vol_Video                    0.015984\n",
      "vol_gratuito_perc            0.013507\n",
      "tot_duracao_chamada          0.012726\n",
      "vol_Loja_de_Apps             0.012286\n",
      "vol_Comunicacao              0.011563\n",
      "vol_Alimentacao              0.011303\n",
      "vol_ul_perc                  0.010798\n",
      "eficiencia_Voz               0.010415\n",
      "DISP_STATUS_p                0.009635\n",
      "ACD_STATUS_p                 0.009568\n",
      "vol_Google                   0.007146\n",
      "OCUP_STATUS_p                0.006293\n",
      "vol_Transporte               0.006269\n",
      "dias_consumo_bonus_ctrl      0.005866\n",
      "vol_InternetBank             0.004583\n",
      "CUSTOMER_SEGMENT_Pós-pago    0.004321\n",
      "vol_Ecommerce                0.004171\n",
      "vol_Jogos                    0.002288\n",
      "device_tech_5G               0.002281\n",
      "FLAG_ELEGIBILIDADE_R         0.002024\n",
      "term_fabr_apple              0.001918\n",
      "SURVEY_Uso Dados             0.001748\n",
      "vol_Viagem                   0.001532\n",
      "SURVEY_Base                  0.001488\n",
      "term_fabr_samsung            0.001020\n",
      "device_tech_4G               0.001014\n",
      "term_fabr_motorola           0.000662\n",
      "term_fabr_outros             0.000260\n",
      "term_fabr_xiaomi             0.000216\n",
      "term_fabr_não informado      0.000136\n",
      "FLAG_PARAMETRO_R             0.000073\n",
      "dias_traf_red                0.000000\n",
      "term_fabr_asus               0.000000\n",
      "term_fabr_lg                 0.000000\n",
      "device_tech_2G               0.000000\n",
      "device_tech_3G               0.000000\n",
      "imsi_LTE                     0.000000\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "feature_importances = pd.DataFrame(best_model_rf.feature_importances_,\n",
    "                                    index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma das features mais importantes\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.set_style('white')\n",
    "sns.set(font_scale=1.4)\n",
    "sns.barplot(x=feature_importances.index, y=feature_importances['importance'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}