Random XGBoost Model Generation for Classification/Regression

This function generates a random XGBoost model for classification or regression tasks. It takes a feature matrix X and a label vector y as input and returns a trained XGBoost model.

Technology Stack : XGBoost, NumPy

Code Type : Custom function

Code Difficulty : Intermediate

                
                    
import xgboost as xgb
import numpy as np
import random

def generate_random_model(X, y, num_rounds=100):
    """
    Generates a random XGBoost model.
    """
    # Define the parameters for the XGBoost model
    params = {
        'max_depth': random.randint(1, 6),
        'eta': random.uniform(0.01, 0.3),
        'subsample': random.uniform(0.5, 1.0),
        'colsample_bytree': random.uniform(0.5, 1.0)
    }
    
    # Create the DMatrix
    dtrain = xgb.DMatrix(X, label=y)
    
    # Train the model
    bst = xgb.train(params, dtrain, num_rounds)
    
    return bst