# Catboost

> CatBoost gradient boosting with categoricals. Use for tabular ML.

- Skill: `g1joshi/catboost` (Agent Skill)
- Install (CLI): `npx skillmds@latest add g1joshi/catboost`
- Raw SKILL.md: https://api.skillmd.com/api/skills/g1joshi/catboost/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/catboost

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# CatBoost

CatBoost (Yandex) is arguably the easiest boosting library to use because it handles **Categorical Features** automatically and perfectly without tuning.

## When to Use

- **Categorical Data**: If you have many strings/IDs, CatBoost is king.
- **Default Params**: Works incredibly well out of the box.

## Core Concepts

### Ordered Boosting

A technique to avoid target leakage (overfitting) during training.

### Symmetric Trees

Builds balanced trees, which are faster at inference time.

## Best Practices (2025)

**Do**:

- **Use pool**: `Pool()` is efficient for data loading.
- **Use GPU**: CatBoost's GPU implementation is highly optimized.

**Don't**:

- **Don't One-Hot Encode**: Let CatBoost handle it natively.

## References

- [CatBoost Documentation](https://catboost.ai/)