Xgboost
XGBoost gradient boosting library. Use for tabular ML.From its SKILL.md
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SKILL.md
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XGBoost
XGBoost is the winningest algorithm in Kaggle history for tabular data. v2.1 (2025) brings native Blackwell GPU support and Polars integration.
When to Use
- Tabular Data: It usually beats Deep Learning on structured tables.
- Speed: Extremely optimized C++ backend.
Core Concepts
Gradient Boosting
Building extensive decision trees sequentially, each correcting the previous one's errors.
DMatrix
Internal optimized data structure.
Device Parameter
device="cuda" enables GPU acceleration.
Best Practices (2025)
Do:
- Use
device="cuda": GPU training is 10x faster. - Use Early Stopping: Stop training when validation error rises.
- Pass Polars Dataframes: No need to convert to Pandas/NumPy first.
Don't:
- Don't use one-hot encoding: Use native categorical support (
enable_categorical=True).
References
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.