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