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Super Data Science: ML & AI Podcast with Jon Krohn
771: Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko
Apr 2, 2024
Machine learning expert Kirill Eremenko discusses decision trees, random forests, and the top gradient boosting algorithms: XGBoost, LightGBM, and CatBoost. Topics include advantages of XGBoost, LightGBM efficiency, and CatBoost's handling of categorical variables.
01:55:27
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Quick takeaways
- Gradient boosting iteratively improves predictions by focusing on residuals and gradients.
- XGBoost prioritizes speed in gradient boosting with efficient enhancements.
Deep dives
Transition to Gradient Boosting from Previous Methods
Unlike random forest, gradient boosting focuses on iteratively improving predictions by predicting errors and chaining models together. Adaboost, a precursor, adapted to model errors selectively. In gradient boosting, each model in the ensemble predicts the gradient of the loss function, optimizing directionally based on residuals.
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