Training a LightGBM Regression Model

This function uses the LightGBM library to train a regression model, accepting a training dataset and a set of parameters as input.

Technology Stack : LightGBM, NumPy

Code Type : Function

Code Difficulty : Intermediate

                
                    
import lightgbm as lgb
import numpy as np

def train_lightgbm_model(X_train, y_train, num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100):
    """
    Train a LightGBM model with given parameters.
    """
    # Create a LightGBM dataset
    train_data = lgb.Dataset(X_train, label=y_train)
    
    # Create a LightGBM model
    gbm = lgb.train({
        'objective': 'regression',  # or 'binary', 'multiclass'
        'num_leaves': num_leaves,
        'max_depth': max_depth,
        'learning_rate': learning_rate,
        'n_estimators': n_estimators
    },
    train_data)
    
    return gbm