Training a CatBoost Classifier in Python

The function uses the CatBoostClassifier from the CatBoost library to train a classification model. It first initializes a CatBoost classifier, then creates a Pool object for the training data, and finally fits the model on the training data.

Technology Stack : Packages and technologies used in the code[English]: CatBoost library, Numpy library

Code Type : The type of code

Code Difficulty :

                
                    
import numpy as np
from catboost import CatBoostClassifier, Pool

def train_catboost_classifier(X_train, y_train):
    # Initialize the CatBoostClassifier
    model = CatBoostClassifier()

    # Create a Pool object for the training data
    train_pool = Pool(data=X_train, label=y_train)

    # Fit the model on the training data
    model.fit(train_pool)

    return model