Training CatBoost Classification Model

This function uses the CatBoost library to train a classification model. It takes a feature matrix X and a label vector y as input, splits the dataset into training and validation sets, and then trains a CatBoost classifier.

Technology Stack : CatBoost, scikit-learn

Code Type : Function

Code Difficulty : Intermediate

                
                    
import catboost as cb
from sklearn.model_selection import train_test_split

def train_catboost_model(X, y):
    # Split the data into training and validation sets
    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # Create a CatBoost model
    model = cb.CatBoostClassifier()
    
    # Train the model
    model.fit(X_train, y_train, eval_set=(X_val, y_val), verbose=10)
    
    return model