Catboost
CatBoost gradient boosting with categoricals. Use for tabular ML.
Install
$ agentstack add skill-g1joshi-agent-skills-catboost ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-g1joshi-agent-skills-catboost) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: G1Joshi
- Source: G1Joshi/Agent-Skills
- License: MIT
- Homepage: https://skills.sh/g1joshi/agent-skills
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.