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/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/343...). And almost top-10 in this competition used LightGBM as sub-models for the ensemble.

I'd recommend rewording the last sentence as it seems to imply that training speed has same "hard to reproduce" numbers, when in fact I believe you meant to say that LightGBM is indeed faster.

Yes, fair point. Edited, thank.

Actually, that's very, very strange. The edit I made doesn't seem to be what is above. I said something like " same (edit: that LightGBM is faster to train than XGB) thing". I mean it's harmless, but very odd.

Did someone else edit my post? @dang ?

This is off topic but I have been told if you have questions to the site you email it to them. I think the contact email is at the bottom.