Training Binary Classifier with XGBoost

This function uses the XGBoost library to train a binary classifier. It accepts training data X_train and corresponding labels y_train, and returns the trained XGBoost classifier.

Technology Stack : XGBoost, Numpy

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
import xgboost as xgb
import numpy as np

def train_xgb_classifier(X_train, y_train):
    # Define the parameters for the XGBoost classifier
    params = {
        'max_depth': 3,
        'eta': 0.1,
        'objective': 'binary:logistic',
        'eval_metric': 'logloss'
    }
    
    # Create an XGBoost classifier
    xgb_clf = xgb.XGBClassifier(**params)
    
    # Fit the classifier to the training data
    xgb_clf.fit(X_train, y_train)
    
    return xgb_clf