here is a benchmark for catboost: https://github.com/szilard/GBM-perf/issues/4
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:…
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…
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 LightGBM will be…
here is a benchmark for catboost: https://github.com/szilard/GBM-perf/issues/4
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:…
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…
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 LightGBM will be…