credit_fraud_detector.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step 1 : Import libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import precision_recall_curve, auc, confusion_matrix, classification_report\n",
    "from imblearn.over_sampling import SMOTE\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Data preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per feature:\n",
      "Time      0\n",
      "V1        0\n",
      "V2        0\n",
      "V3        0\n",
      "V4        0\n",
      "V5        0\n",
      "V6        0\n",
      "V7        0\n",
      "V8        0\n",
      "V9        0\n",
      "V10       0\n",
      "V11       0\n",
      "V12       0\n",
      "V13       0\n",
      "V14       0\n",
      "V15       0\n",
      "V16       0\n",
      "V17       0\n",
      "V18       0\n",
      "V19       0\n",
      "V20       0\n",
      "V21       0\n",
      "V22       0\n",
      "V23       0\n",
      "V24       0\n",
      "V25       0\n",
      "V26       0\n",
      "V27       0\n",
      "V28       0\n",
      "Amount    0\n",
      "Class     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values = data.isnull().sum()\n",
    "print(\"Missing values per feature:\")\n",
    "print(missing_values)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for and handle duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows: 1081\n",
      "Number of duplicate rows after handling: 0\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "duplicates = data.duplicated().sum()\n",
    "print(f\"Number of duplicate rows: {duplicates}\")\n",
    "\n",
    "# Drop duplicates (if needed)\n",
    "data.drop_duplicates(inplace=True)\n",
    "\n",
    "#Check for duplicate after handling\n",
    "duplicates = data.duplicated().sum()\n",
    "print(f\"Number of duplicate rows after handling: {duplicates}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify and handle outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outlier rows: 37930\n"
     ]
    }
   ],
   "source": [
    "# Z-score method for detecting outliers\n",
    "from scipy import stats\n",
    "z_scores = np.abs(stats.zscore(data))\n",
    "outliers = (z_scores > 3).any(axis=1)\n",
    "print(f\"Number of outlier rows: {outliers.sum()}\")\n",
    "\n",
    "# In the context of fraud detection I decide to keep the outliers because they are more likely to be fraudulent transactions\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardize numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Standardize the 'Time' and 'Amount' columns in the dataset\n",
    "# This is done to ensure that these numerical features are on the same scale, which can help improve the performance of the model.\n",
    "# StandardScaler transforms the data by subtracting the mean and dividing by the standard deviation.\n",
    "data[['Time', 'Amount']] = scaler.fit_transform(data[['Time', 'Amount']])\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Data splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the features (X) and the target variable (y)\n",
    "# In this case, we are dropping the 'Class' column, which is the target variable, from the dataset to create the feature matrix (X)\n",
    "# 'Class' column is used as the target variable (y)\n",
    "X = data.drop('Class', axis=1)\n",
    "y = data['Class']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "# The test set size is 20% of the total dataset, and the train set size is 80%\n",
    "# 'stratify=y' ensures that the distribution of the target variable (fraud vs. non-fraud) is preserved in both the train and test sets\n",
    "# 'random_state=42' sets a seed for reproducibility of the random split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: Handling imbalance data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the SMOTE (Synthetic Minority Over-sampling Technique) algorithm with a random seed for reproducibility\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "# Use SMOTE to resample the training dataset by generating synthetic samples for the minority class (fraud cases)\n",
    "# This helps to balance the class distribution in the training data, which can improve the model's performance on the minority class\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: Model Selection and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/ML/lib/python3.8/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish training LR\n",
      "Finish training RF\n",
      "Finisn training KNN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/ML/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/opt/anaconda3/envs/ML/lib/python3.8/site-packages/xgboost/data.py:250: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:57:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Finish training XGBoost\n",
      "Finsih training lightBGM\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "# 1. Logistic Regression\n",
    "# Reason: It's a simple, fast, and interpretable linear model that works well when the relationship between the features and the target is approximately linear.\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "log_reg.fit(X_train_resampled, y_train_resampled)\n",
    "print(\"Finish training LR\")\n",
    "\n",
    "# 2. Random Forest\n",
    "# Reason: It's an ensemble method that works well with high-dimensional data and can capture complex patterns by combining multiple decision trees.\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "rf.fit(X_train_resampled, y_train_resampled)\n",
    "print(\"Finish training RF\")\n",
    "\n",
    "# 3. K-Nearest Neighbors (KNN)\n",
    "# Reason: It's a non-parametric method that can capture non-linear relationships in the data by considering the \"neighborhood\" of data points.\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train_resampled, y_train_resampled)\n",
    "print(\"Finisn training KNN\")\n",
    "\n",
    "# 4. XGBoost\n",
    "# Reason: It's an efficient gradient boosting algorithm that can handle a wide range of data and is known for its high performance and speed.\n",
    "xgb = XGBClassifier(random_state=42)\n",
    "xgb.fit(X_train_resampled, y_train_resampled)\n",
    "print(\"Finish training XGBoost\")\n",
    "\n",
    "# 5. LightGBM\n",
    "# Reason: It's a gradient boosting framework that uses tree-based learning algorithms and is designed for large datasets, offering better efficiency and speed than other gradient boosting methods.\n",
    "lgbm = LGBMClassifier(random_state=42)\n",
    "lgbm.fit(X_train_resampled, y_train_resampled)\n",
    "print(\"Finsih training lightBGM\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 7: Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Evaluation:\n",
      "AUPRC: 0.4634823664422642\n",
      "Confusion Matrix:\n",
      "[[55170  1481]\n",
      " [   12    83]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99     56651\n",
      "           1       0.05      0.87      0.10        95\n",
      "\n",
      "    accuracy                           0.97     56746\n",
      "   macro avg       0.53      0.92      0.54     56746\n",
      "weighted avg       1.00      0.97      0.99     56746\n",
      "\n",
      "Random Forest Evaluation:\n",
      "AUPRC: 0.8348462284087304\n",
      "Confusion Matrix:\n",
      "[[56644     7]\n",
      " [   23    72]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56651\n",
      "           1       0.91      0.76      0.83        95\n",
      "\n",
      "    accuracy                           1.00     56746\n",
      "   macro avg       0.96      0.88      0.91     56746\n",
      "weighted avg       1.00      1.00      1.00     56746\n",
      "\n",
      "KNN Evaluation:\n",
      "AUPRC: 0.6456158651192072\n",
      "Confusion Matrix:\n",
      "[[56563    88]\n",
      " [   17    78]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56651\n",
      "           1       0.47      0.82      0.60        95\n",
      "\n",
      "    accuracy                           1.00     56746\n",
      "   macro avg       0.73      0.91      0.80     56746\n",
      "weighted avg       1.00      1.00      1.00     56746\n",
      "\n",
      "XGBoost Evaluation:\n",
      "AUPRC: 0.7625601248036031\n",
      "Confusion Matrix:\n",
      "[[56624    27]\n",
      " [   20    75]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56651\n",
      "           1       0.74      0.79      0.76        95\n",
      "\n",
      "    accuracy                           1.00     56746\n",
      "   macro avg       0.87      0.89      0.88     56746\n",
      "weighted avg       1.00      1.00      1.00     56746\n",
      "\n",
      "LightGBM Evaluation:\n",
      "AUPRC: 0.688509994676184\n",
      "Confusion Matrix:\n",
      "[[56592    59]\n",
      " [   18    77]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56651\n",
      "           1       0.57      0.81      0.67        95\n",
      "\n",
      "    accuracy                           1.00     56746\n",
      "   macro avg       0.78      0.90      0.83     56746\n",
      "weighted avg       1.00      1.00      1.00     56746\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define a function to evaluate the performance of a given model on the test dataset\n",
    "def evaluate_model(model, X_test, y_test):\n",
    "    # Predict the target class for the test dataset\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate the precision-recall curve and the area under the curve (AUPRC)\n",
    "    precision, recall, _ = precision_recall_curve(y_test, y_pred)\n",
    "    auprc = auc(recall, precision)\n",
    "    print(f\"AUPRC: {auprc}\")\n",
    "    \n",
    "    # Display the confusion matrix and classification report for the model's performance\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print(\"Classification Report:\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Evaluate and display the performance of each trained model on the test dataset\n",
    "print(\"Logistic Regression Evaluation:\")\n",
    "evaluate_model(log_reg, X_test, y_test)\n",
    "\n",
    "print(\"Random Forest Evaluation:\")\n",
    "evaluate_model(rf, X_test, y_test)\n",
    "\n",
    "print(\"KNN Evaluation:\")\n",
    "evaluate_model(knn, X_test, y_test)\n",
    "\n",
    "print(\"XGBoost Evaluation:\")\n",
    "evaluate_model(xgb, X_test, y_test)\n",
    "\n",
    "print(\"LightGBM Evaluation:\")\n",
    "evaluate_model(lgbm, X_test, y_test)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the given results, the Random Forest model performs the best among the selected models. The reasoning behind this conclusion is as follows:\n",
    "\n",
    "1. AUPRC (Area Under the Precision-Recall Curve): Random Forest has the highest AUPRC (0.8348) among all models, indicating better overall performance in distinguishing between the classes when dealing with imbalanced data.\n",
    "\n",
    "2. Precision, Recall, and F1-score: Random Forest has the highest precision (0.91) for the positive class (fraud), which means it has the lowest false positive rate among the models. It also has a good recall (0.76), which means it can detect a considerable proportion of the actual fraud cases. The F1-score (0.83) for the positive class in Random Forest is also the highest, indicating a good balance between precision and recall.\n",
    "\n",
    "3. Confusion Matrix: The confusion matrix of the Random Forest model shows the smallest number of false positives (7) and a relatively low number of false negatives (23) compared to other models.\n",
    "\n",
    "Although the accuracy is high for all the models, it is not a reliable metric in this case due to the highly imbalanced nature of the dataset. The other metrics mentioned above provide a better perspective on the model's performance.\n",
    "\n",
    "Considering all these factors, the Random Forest model seems to be the best performer among the selected models for this imbalanced dataset."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 8: Parameter Tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=16.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=16.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=11.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=21.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 5.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 5.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 5.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=21.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=21.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=21.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=21.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=14.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=14.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=10.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=10.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=10.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=10.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=15.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=15.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=15.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=15.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=15.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 9.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=15.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=15.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=14.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=14.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=15.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=10.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=10.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 5.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=15.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=15.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=15.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=15.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=15.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.8min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=13.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=13.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=14.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.6min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.3min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 6.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 6.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 6.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 6.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 6.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 6.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 6.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 3.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=10.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=19.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=42.0min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=42.1min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=35.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=56.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=56.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=53.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=68.1min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=52.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=51.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=51.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=18.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 3.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 3.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=54.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=41.1min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=41.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=41.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=27.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=17.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=17.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=11.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 3.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 3.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.3min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.3min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 9.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 9.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=24.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=24.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=34.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=18.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=18.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=43.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=42.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=42.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 3.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=42.5min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=42.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.3min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=15.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=15.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=16.1min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=16.6min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=16.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=12.0min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=12.3min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=12.2min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=12.3min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=12.4min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=18.6min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 8.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 8.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 8.7min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=18.8min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=18.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=18.9min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 8.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 8.9min\n",
      "[CV] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=19.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=17.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=17.9min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=17.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=17.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=17.9min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=26.9min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=27.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=27.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=27.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=27.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=19.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=19.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=19.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=19.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=19.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=28.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=28.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=28.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=28.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=28.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=19.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=19.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=19.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=19.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=19.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=29.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=29.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=29.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 9.9min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=10.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=28.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=29.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=19.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=19.6min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=19.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=19.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=19.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 9.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 9.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=29.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=29.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=29.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=11.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=11.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=31.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=31.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=21.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=21.6min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=21.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=21.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=19.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=10.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=10.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=30.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=30.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=30.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=30.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=29.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=16.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=14.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=12.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=12.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=15.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=12.9min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=12.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=12.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=12.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.3min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.6min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=14.8min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=15.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=15.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.4min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 5.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=15.2min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=15.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=10.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=22.7min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=22.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=31.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=19.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=19.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=41.5min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=41.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=41.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=41.1min\n",
      "[CV] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=40.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=14.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=14.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=14.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=14.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=14.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=14.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=14.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=14.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 5.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=15.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=15.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=10.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=15.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=14.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=14.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=14.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=14.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 5.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 5.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 5.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=30.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=30.3min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=30.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=20.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=20.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=34.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=34.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=33.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=18.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=20.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=20.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=21.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=10.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=10.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=10.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=33.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=32.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=32.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=10.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=10.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=32.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=32.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=19.9min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=19.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=16.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=15.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=15.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=20.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=16.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=16.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=14.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=14.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.1min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.0min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=12.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=12.7min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=12.8min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=12.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=12.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=12.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=12.4min\n",
      "[CV] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=12.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=12.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=12.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=12.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=12.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=13.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=19.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=19.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=19.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=19.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=20.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=20.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=20.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=20.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=20.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=20.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=13.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=20.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=20.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=20.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=20.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=20.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=20.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=20.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=20.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 6.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=19.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=20.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=13.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=13.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=13.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=19.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=20.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=20.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 7.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=20.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=20.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=14.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=13.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=14.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 6.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=20.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=20.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=21.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=14.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=14.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=15.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 8.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 8.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=23.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 8.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=24.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=24.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 7.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 7.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=23.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=24.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=14.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=15.0min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=14.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=14.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=14.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 7.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 7.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 7.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=21.2min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=21.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=21.1min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 6.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 7.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=21.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=21.8min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=15.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=15.5min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=15.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 5.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 5.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=22.3min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=21.9min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=21.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 8.7min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=20.4min\n",
      "[CV] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=20.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=12.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.1min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.1min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=13.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time= 9.1min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time= 9.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 5.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=14.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=14.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=14.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 4.7min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=14.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=14.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=10.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=10.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=10.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=14.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=14.1min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=13.1min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=12.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=12.8min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 4.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=12.7min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=12.7min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 8.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 8.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.2min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.3min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 4.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 8.9min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time= 9.0min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 6.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=13.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=19.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=19.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=19.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 6.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=20.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=20.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=13.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=20.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=19.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 6.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=19.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=19.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=19.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=12.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=12.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=12.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=12.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=12.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=19.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=19.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=19.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=19.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=19.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=13.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=37.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=50.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=50.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=19.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 7.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 7.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=57.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=58.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=57.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 7.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 7.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=58.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=58.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=15.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=15.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=15.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=15.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 7.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 7.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 7.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=22.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=23.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=23.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 7.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 7.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=22.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=23.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=15.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=15.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=15.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=23.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=23.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=22.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 7.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=22.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=22.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=14.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=14.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=14.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 7.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 7.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 7.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=20.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=21.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 6.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=21.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 6.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=20.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=21.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=13.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=14.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 7.0min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 7.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 7.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=21.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=22.2min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=22.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 8.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 8.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=23.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=23.6min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=16.7min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=16.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=16.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=16.3min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=16.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=24.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=24.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=39.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=70.5min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=70.4min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=70.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=38.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=39.8min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=73.1min\n",
      "[CV] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=73.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=41.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=27.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=29.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=30.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=31.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=16.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=17.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=17.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=49.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=51.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=51.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=18.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=18.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=52.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=54.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=37.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=37.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=37.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=36.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=37.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=17.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=17.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=17.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=54.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=54.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=17.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=54.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=18.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=53.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=54.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=35.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=35.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=35.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=35.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=36.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=17.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=17.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=53.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=17.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=54.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=54.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=17.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=18.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=53.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=54.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=35.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=36.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=36.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=36.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=37.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=17.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=17.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=17.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=53.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=53.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=52.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=16.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=16.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=51.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=52.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=33.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=34.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=34.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=34.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=35.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=17.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=17.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=33.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=82.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=86.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=85.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=48.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=47.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=78.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=78.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=42.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=27.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=22.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=23.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=24.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=13.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=13.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=39.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=40.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=13.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=41.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=14.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=41.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=42.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=28.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=28.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=28.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=28.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=29.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=14.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=14.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=42.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=14.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=43.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=14.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=43.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=14.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=43.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=43.9min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=28.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=28.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=28.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=28.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=28.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=14.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=14.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=14.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=42.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=42.8min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=42.6min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=14.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=14.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=42.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=43.1min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=28.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=28.4min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=28.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=28.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=28.7min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=38.2min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=37.0min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=36.5min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=30.3min\n",
      "[CV] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=29.2min\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the hyperparameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'bootstrap': [True, False]\n",
    "}\n",
    "\n",
    "# Create the base model\n",
    "rf_base = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Create the grid search object\n",
    "grid_search = GridSearchCV(estimator=rf_base, param_grid=param_grid, scoring='recall', cv=5, verbose=2, n_jobs=-1)\n",
    "\n",
    "# Fit the grid search object to the resampled data\n",
    "grid_search.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Get the best hyperparameters\n",
    "best_params = grid_search.best_params_\n",
    "\n",
    "# Train the model with the best hyperparameters\n",
    "rf_tuned = RandomForestClassifier(**best_params, random_state=42)\n",
    "rf_tuned.fit(X_train_resampled, y_train_resampled)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing result of pre-tuned and after-tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Random Forest Evaluation:\n",
      "AUPRC: 0.8077696451432539\n",
      "Confusion Matrix:\n",
      "[[56644     7]\n",
      " [   23    72]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56651\n",
      "           1       0.91      0.76      0.83        95\n",
      "\n",
      "    accuracy                           1.00     56746\n",
      "   macro avg       0.96      0.88      0.91     56746\n",
      "weighted avg       1.00      1.00      1.00     56746\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred_tuned = rf_tuned.predict(X_test)\n",
    "y_pred_proba_tuned = rf_tuned.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate the AUPRC\n",
    "auprc_tuned = average_precision_score(y_test, y_pred_proba_tuned)\n",
    "\n",
    "# Print the AUPRC\n",
    "print(\"Tuned Random Forest Evaluation:\")\n",
    "print(f\"AUPRC: {auprc_tuned}\")\n",
    "\n",
    "# Calculate and print the confusion matrix\n",
    "conf_matrix_tuned = confusion_matrix(y_test, y_pred_tuned)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_tuned)\n",
    "\n",
    "# Calculate and print the classification report\n",
    "class_report_tuned = classification_report(y_test, y_pred_tuned)\n",
    "print(\"Classification Report:\")\n",
    "print(class_report_tuned)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the pre-tuned and after-tuned results, we see that the AUPRC, confusion matrix, and classification report values are very similar.\n",
    "\n",
    "Pre-tuned Random Forest:\n",
    "\n",
    "AUPRC: 0.8348462284087304\n",
    "\n",
    "Confusion Matrix: [[56644, 7], [23, 72]]\n",
    "\n",
    "Precision (class 1): 0.91\n",
    "\n",
    "Recall (class 1): 0.76\n",
    "\n",
    "F1-score (class 1): 0.83\n",
    "\n",
    "After-tuned Random Forest:\n",
    "\n",
    "AUPRC: 0.8077696451432539\n",
    "\n",
    "Confusion Matrix: [[56644, 7], [23, 72]]\n",
    "\n",
    "Precision (class 1): 0.91\n",
    "\n",
    "Recall (class 1): 0.76\n",
    "\n",
    "F1-score (class 1): 0.83\n",
    "\n",
    "The metrics show that the performance of the tuned Random Forest model is almost the same as the base model, with a slightly lower AUPRC. The precision, recall, and F1-score for the minority class (class 1) are identical for both models.\n",
    "\n",
    "In this case, it appears that hyperparameter tuning did not lead to a significant improvement in the model's performance."
   ]
  }
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