lightgbm

LightGBM gradient boosting framework. Use for fast ML.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

lightgbm is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

LightGBM gradient boosting framework. Use for fast ML.

Teams using lightgbm should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/lightgbm/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/lightgbm/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/lightgbm/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How lightgbm Compares

Feature / Agent	lightgbm	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

LightGBM gradient boosting framework. Use for fast ML.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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/)