Machine Learning with Tree-Based Models in Python__Model Tuning.py

# -*- coding: utf-8 -*-
"""
Created on Wed Aug 21 07:43:50 2019

@author: z
"""

###Model Tuning
##Set the tree's hyperparameter grid
# Define params_dt
params_dt = {
             'max_depth': [2, 3, 4],
             'min_samples_leaf': [0.12, 0.14, 0.16, 0.18]
            }

##Search for the optimal tree
# Import GridSearchCV
from sklearn.model_selection import GridSearchCV

# Instantiate grid_dt
grid_dt = GridSearchCV(estimator=dt,
                       param_grid=params_dt,
                       scoring='roc_auc',
                       cv=5,
                       n_jobs=-1)

##Evaluate the optimal tree
# Import roc_auc_score from sklearn.metrics 
from sklearn.metrics import roc_auc_score

# Extract the best estimator
best_model = grid_dt.best_estimator_

# Predict the test set probabilities of the positive class
y_pred_proba = best_model.predict_proba(X_test)[:,1]

# Compute test_roc_auc
test_roc_auc = roc_auc_score(y_test, y_pred_proba)

# Print test_roc_auc
print('Test set ROC AUC score: {:.3f}'.format(test_roc_auc))


##Set the hyperparameter grid of RF
# Define the dictionary 'params_rf'
params_rf = {
             'n_estimators': [100, 350, 500],
             'max_features': ['log2', 'auto', 'sqrt'],
             'min_samples_leaf': [2, 10, 30], 
             }


##Search for the optimal forest
# Import GridSearchCV
from sklearn.model_selection import  GridSearchCV

# Instantiate grid_rf
grid_rf = GridSearchCV(estimator=rf,
                       param_grid=params_rf,
                       scoring='neg_mean_squared_error',
                       cv=3,
                       verbose=1,
                       n_jobs=-1)

##Evaluate the optimal forest
# Import mean_squared_error from sklearn.metrics as MSE 
from sklearn.metrics import mean_squared_error as MSE

# Extract the best estimator
best_model = grid_rf.best_estimator_

# Predict test set labels
y_pred = best_model.predict(X_test)

# Compute rmse_test
rmse_test = MSE(y_test, y_pred)**(1/2)

# Print rmse_test
print('Test RMSE of best model: {:.3f}'.format(rmse_test))