Random Forest Classification with CatBoost

This function uses the CatBoost library to train a random forest classifier, trains the model on the given training set, and then makes predictions on the test set.

Technology Stack : CatBoost

Code Type : Machine learning classification function

Code Difficulty : Intermediate

                
                    
def random_forest_classification(X_train, y_train, X_test, n_estimators=100, max_depth=6):
    from catboost import CatBoostClassifier
    
    # Train a CatBoost classifier
    model = CatBoostClassifier(n_estimators=n_estimators, max_depth=max_depth)
    model.fit(X_train, y_train)
    
    # Predict on the test set
    predictions = model.predict(X_test)
    
    return predictions