G1Joshi LightGBM
LightGBM is Microsoft's gradient boosting library. It is often faster and uses less memory than XGBoost due to leaf-wise tree growth.
When to Use
- Huge Datasets: Optimized for efficiency.
- Ranking:
LGBMRankeris excellent for search/recommendation systems.
Core Concepts
Leaf-wise Growth
Grows the tree by splitting the leaf with max loss delta (creates deeper, unbalanced trees) vs Level-wise (balanced).
Histogram-based
Buckets continuous values into discrete bins for speed.
Best Practices (2025)
Do:
- Tune
num_leaves: The most important parameter for controlling complexity. - Use Categorical Features: Pass indexes of categorical columns directly.
Don't:
- Don't overfit: Leaf-wise growth overfits easily on small data. Limit
max_depth.