While algorithms like XGBoost provide significant performance gains over standard Gradient Boosting Machines, they can still face computational bottlenecks when dealing with very large datasets and high-dimensional feature spaces. This chapter introduces LightGBM, a framework engineered specifically to address these challenges, prioritizing training speed and memory efficiency without substantial compromises in accuracy.

You will study the core techniques that contribute to LightGBM's efficiency. We will cover:

• Gradient-based One-Side Sampling (GOSS): A method to selectively focus on data instances with larger gradients, reducing computation while preserving model accuracy.
• Exclusive Feature Bundling (EFB): An approach to bundle mutually exclusive features, effectively reducing the number of features considered during training.
• Histogram-based Algorithm: How LightGBM uses discretized feature values (histograms) to accelerate the process of finding optimal split points.
• Leaf-wise Tree Growth: Contrasting LightGBM's strategy of growing trees leaf-by-leaf against the more common level-wise approach, and understanding its impact on performance and potential for overfitting.
• Optimized Categorical Feature Handling: Examining LightGBM's native support for categorical variables.

The chapter will also guide you through the essential parameters of the LightGBM Python API and culminate in a practical exercise where you implement and train a LightGBM model.