Dear All,
You can now use LightGBM algorithm, however the predefined models we released are not correct. I did a simple test using all three of them and got very decent performances which is encouraging. The best was LightGBM II, which also ran the fastest of the three. The slowest was LigthGBM I, so definitely the parameters need work.
We are working to fix the hyperparameters but it's time consuming. We decided not to hide the predefined models while we fix them hoping to get your suggestions. Also any suggestions to improve other predefined models are welcome (tuning models seems to be more of an art)
Thank You
The current definitions are these
LightGBM I
"n_estimators": 100, "max_depth": 4, "learning_rate": 0.1, "num_leaves": 16, "subsample": 0.8, "colsample_bytree": 0.8, "min_child_samples": 20
LightGBM II
"n_estimators": 300, "max_depth": 6, "learning_rate": 0.05, "num_leaves": 32, "subsample": 0.7, "colsample_bytree": 0.7, "min_child_samples": 25, "reg_alpha": 0.3, "reg_lambda": 0.3, "bagging_freq": 5
LightGBM III
"n_estimators": 500, "max_depth": 12, "learning_rate": 0.01, "num_leaves": 128, "subsample": 0.6, "colsample_bytree": 0.6, "min_child_samples": 10
The obvious problem is min_child_samples since the progression from I, II, III is 20, 25, 10.
Also LightGBM II has parameters not present in the other two models.