PYTHON

sample hyperparameter tuning with grid search cv

# Define params_dt
params_dt = {
             'max_depth': [2, 3, 4],
             'min_samples_leaf': [0.12, 0.14, 0.16, 0.18]
            }
# Import GridSearchCV
from sklearn.model_selection import GridSearchCV

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

#Train the model
grid_dt.fit(X_train, y_train)

# 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))