A comprehensive guide to understanding and implementing gradient boosting models. This course covers the theoretical underpinnings of boosting and provides practical instruction on using popular libraries such as Scikit-Learn, XGBoost, LightGBM, and CatBoost. You will learn to build, interpret, and optimize high-performance models for both regression and classification tasks.
Prerequisites Python and ML concepts
Level:
Boosting Mechanics
Explain the sequential nature of boosting algorithms and how they differ from other ensemble methods.
Gradient Boosting Implementation
Implement Gradient Boosting Machines (GBM) for regression and classification using the Scikit-Learn library.
Advanced Libraries
Utilize modern, optimized gradient boosting libraries including XGBoost, LightGBM, and CatBoost to train models efficiently.
Hyperparameter Optimization
Apply structured techniques like Grid Search and Randomized Search to tune model hyperparameters for improved performance.
Model Interpretation
Analyze feature importance and use partial dependence plots to understand model predictions.
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