Xgboost Lightgbm
These are the libraries you reach for when you have tabular data and need accurate predictions. XGBoost and LightGBM dominate Kaggle competitions and production ML systems because they handle the messy realities of real data: missing values, imbalanced classes, categorical features, and they give you interpretable feature importances out of the box. The key difference is speed versus dataset size. XGBoost is slower but often slightly more accurate on smaller datasets under 100k rows, while LightGBM's histogram-based approach makes it significantly faster on millions of rows. Both include built-in regularization and early stopping, which you should always use. If you're doing serious work with CSVs, databases, or any structured data, you need at least one of these in your toolkit.
- skills.sh installs
- 488
- GitHub stars
- 25
- last updated
- Aug 21, 2026
Install
$ npx -y skills add tondevrel/scientific-agent-skills --skill xgboost-lightgbm --agent claude-codeInstalls into .claude/skills of the current project.
Files
XGBoost & LightGBM - Gradient Boosting for Tabular Data
XGBoost (eXtreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine) are the de facto standard libraries for machine learning on tabular/structured data. They consistently win Kaggle competitions and are widely used in industry for their speed, accuracy, and robustness.
When to Use
- Classification or regression on tabular data (CSVs, databases, spreadsheets).
- Kaggle competitions or data science competitions on structured data.
- Feature importance analysis and feature selection.
- Handling missing values automatically (no need to impute).
- Working with imbalanced datasets (built-in class weighting).
- Need for fast training on large datasets (millions of rows).
- Hyperparameter tuning with cross-validation.
- Ranking tasks (learning-to-rank algorithms).
- When you need interpretable feature importances.
- Production ML systems requiring fast inference on tabular data.
Reference Documentation
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- Repository
- github.com/tondevrel/scientific-agent-skills
- Categories
- AI & Agent Building, Data Science & ML, Debugging, Office & Documents
- First seen
- Jun 3, 2026
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