Lightgbm
LightGBM gradient boosting framework. Use for fast ML.From its SKILL.md
npx -y skills add G1Joshi/Agent-Skills --skill lightgbmAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
1.0 KB, 214 tokens by cl100k_base, as published. Nobody here has run it
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.
References
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.