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The benchmark scores seem to have been measured against Kaggle dataset which makes the scores more reliable and also with Categorical Features support and less
by amrrs 9y ago
The benchmark scores seem to have been measured against Kaggle dataset which makes the scores more reliable and also with Categorical Features support and less tuning requirement, Catboost might be the ML library XGBoost enthus might have been looking for, but on the contrary, how come a Gradient Boosting Library making news while everyone's talking about Deep learning stuff?
- zitterbewegung 9y agoI think its because of three reasons: 1. Yandex is annoucing a new ML library and that makes it news because Yandex is well established 2. Gradient boosting is quite effective and popular 3. Not everything has to be about deep learning
- daddyo 9y agoAdding to 1.: This came out of MatrixNet research, which was state-of-the-art (and well-guarded) for years.
- visarga 9y agoDeep learning works best on images, video, text, audio and reinforcement learning. Most common applications are outside these domains, based on diverse types of business/scientific data. But, as they said, you can compose CatBoost with TF and Keras neural nets. It connects like a NN module.
- technics256 9y agoDo you have any examples of this or can point me in the right direction of how to implement it? Thanks!
- make3 9y agoAfaik gradient boosting wins lots of Kaggle competitions when they are on small amounts of data. It is possible that gradient boosting is the king of small data, but I'm not an expert on the subject