Random Forest Classification with CatBoost

This function uses CatBoost's CatBoostClassifier to implement random forest classification. It accepts feature matrix X and target vector y as input, and can adjust the maximum tree depth, learning rate, and number of iterations. After training the model, it uses it to predict new data.

Technology Stack : CatBoost, NumPy

Code Type : Function

Code Difficulty : Intermediate

                
                    
def random_forest_classification(X, y, depth=3, learning_rate=0.1, iterations=100):
    import numpy as np
    from catboost import CatBoostClassifier

    # Create a CatBoost Classifier
    model = CatBoostClassifier(depth=depth, learning_rate=learning_rate, iterations=iterations)

    # Train the model
    model.fit(X, y)

    # Predict using the model
    predictions = model.predict(X)

    return predictions