insulator

3 ms·

1. 1.▲
   by insulator 9y ago

   here is a benchmark for catboost: https://github.com/szilard/GBM-perf/issues/4
2. 2.▲
   by insulator 9y ago

   The parameter of LightGBM is a little bit hard to tune. And when #data is small, it is easier over-fitting. But it will be powerful if you tuned it well (Here a guide for the parameter tuning: https://github.com/Microsoft&#x
3. 3.▲
   by insulator 9y ago

   actually, the histogram-based tree learning algorithm doesn't need the sorting. The main cost of it is the memory access. The GPU version is slow (when #data is small) due to the additional memory copy cost between GPU and CPU. I think
4. 4.▲
   by insulator 9y ago

   CatBoost is benefited from categorical feature transform. LightGBM is also working for the better categorical feature support ( https://github.com/Microsoft/LightGBM/issues/699 ). I think the accuracy of Light