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---
name: xgboost
description: XGBoost gradient boosting library. Use for tabular ML.
---
# 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
- [XGBoost Documentation](https://xgboost.readthedocs.io/)
