Mastering Gradient Boosting with XGBoost & LightGBM
Artificial Intelligence
3 courses
39 practices
3 hours
Explore the world of gradient boosting from the ground up! Build, tune, and interpret powerful models using scikit-learn, XGBoost, LightGBM, and CatBoost—gaining hands-on skills to solve real-world classification problems with confidence.
You'll start by building a single decision tree, then see how combining trees in a Random Forest improves results. Finally, you'll learn the sequential approach of Gradient Boosting, building and tuning your first powerful boosting model.
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14 practices
You'll use the popular XGBoost library to build faster, more accurate models. You'll learn to control model complexity, prevent overfitting with early stopping, and automate parameter tuning with Grid Search for peak performance.
You'll explore LightGBM's unique architecture, focusing on its efficient leaf-wise tree growth and histogram-based algorithms. You'll learn how to leverage its key parameters for model control, compare its performance to other boosting libraries, and gain hands-on experience.
