# Lightgbm

> LightGBM gradient boosting framework. Use for fast ML.

- Skill: `g1joshi/lightgbm` (Agent Skill)
- Install (CLI): `npx skillmds@latest add g1joshi/lightgbm`
- Raw SKILL.md: https://api.skillmd.com/api/skills/g1joshi/lightgbm/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: G1Joshi (https://skillmd.com/u/g1joshi)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/g1joshi/lightgbm

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# 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**: `LGBMRanker` is 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

- [LightGBM Documentation](https://lightgbm.readthedocs.io/)