CatBoost - The New Generation of Gradient Boosting

on YouTube ↗01h 27m

" summary

CatBoost claims to outperforms existing implementations on public benchmarks and ships an open source library with CPU and GPU engines, native handling of high‑cardinality categorical features, SHAP support and plotting, cross‑validation, early stopping, snapshots and model export. Benchmarks show the GPU can be 20–40× faster than CPU and competitors and prediction time can be 2–40× faster; examples include an Amazon dataset where CatBoost is sometimes significantly better. The GPU implementation, utilities and exporters are complete and usable.

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" chapters (8)

1. 0:00CatBoost intro
2. 10:12CatBoost advantages
3. 21:40Tutorial setup
4. 38:31First classification model
5. 58:29Cross-validation & early stopping
6. 1:05:43Threshold selection
7. 1:17:55Feature importance
8. 1:23:13Q&A

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