XGBoost_Classifier.py

# Parameters to tune for XGBoost model
params = {'n_estimators': [10, 50, 100, 500, 1000], 
          'max_depth': [2, 3, 4, 5, 6],
          'learning_rate': [0.0001,0.005,0.001,0.05, 0.1],
          'reg_alpha': [1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2], 
          'reg_lambda': [1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2]}

# Create a custom (MCC) metric for evaluation of the model performance while 
# hyperparameter tuning the XGBoost model
mcc = make_scorer(matthews_corrcoef, greater_is_better=True)

# Create an XGBoost classifier object with log-loss as the loss function to minimize
xgb_clf = xgb.XGBClassifier(random_state=42)



# Perform stratified 5-fold cross validation
rand_clf = RandomizedSearchCV(xgb_clf, param_distributions=params, scoring=mcc, 
                              cv=5, random_state=42, return_train_score=True,
                              n_jobs=-1)

# Fit the model
rand_clf.fit(X_train, y_train)
print(f"Best CV score: {rand_clf.best_score_}")

ok