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

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/LightGBM/blob/master/docs/Parameters-tuning.md#tune-parameters-for-the-leaf-wisebest-first-tree https://github.com/Microsoft/LightGBM/blob/master/docs/Param...). Also, there are many kaggle winning solutions using lightgbm recently. e.g. The 2nd on "Quora Question Pairs" used the ensemble of 6 lightGBM and 1 NN (https://www.kaggle.com/c/quora-question-pairs/discussion/34310 https://www.kaggle.com/c/quora-question-pairs/discussion/343...). And almost top-10 in this competition used LightGBM as sub-models for the ensemble.