Random XGBoost Classification with Dataset Splitting

This function uses the XGBoost library to split the given dataset randomly, then trains an XGBoost classifier, and makes predictions on the test set.

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_classification(data):
    # Split the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(data.drop('target', axis=1), data['target'], test_size=0.2, random_state=42)
    
    # Initialize the XGBoost classifier
    xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')
    
    # Train the model
    xgb_model.fit(X_train, y_train)
    
    # Predict the labels for the test set
    predictions = xgb_model.predict(X_test)
    
    # Return the predictions
    return predictions