XGBoost Classification Model Training and Prediction

This function uses the XGBoost library to train a classification model and uses the trained model to make predictions on the test data.

Technology Stack : XGBoost, NumPy, Pandas, Scikit-learn

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
import xgboost as xgb
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

def random_xgb_model(X, y):
    # Split the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Create a DMatrix object for the training data
    dtrain = xgb.DMatrix(X_train, label=y_train)

    # Create a DMatrix object for the testing data
    dtest = xgb.DMatrix(X_test, label=y_test)

    # Define the parameters for the XGBoost model
    params = {
        'max_depth': 3,
        'eta': 0.1,
        'objective': 'binary:logistic',
        'eval_metric': 'logloss'
    }

    # Train the model
    bst = xgb.train(params, dtrain, evals=[(dtest, 'eval')], early_stopping_rounds=10)

    # Make predictions on the test set
    y_pred = bst.predict(dtest)

    return y_pred

# Code Information