I’m doing a benchmark between XGBoost, XGBoost with leaf-wise histogram and LightGBM. These are the results I’m getting:
|Airline subsample size||Lib||Training time (s)||Test time (s)||AUC||F1|
You can see the code here.
Both XGBoost and LightGBM have competitive results, however, XGBoost hist has a considerable performance drop. For example, in the smaller dataset, the AUC in XGB is 0.83, LGB is 0.82, however XGB hist is 0.7.
These are the parameters I’m using for each:
num_rounds = 200 xgb_clf_pipeline = xgb.XGBRegressor(max_depth=8, n_estimators=num_rounds, min_child_weight=30, learning_rate=0.1, scale_pos_weight=2, gamma=0.1, reg_lambda=1, subsample=1, n_jobs=-1, random_state=77) xgb_hist_clf_pipeline = xgb.XGBRegressor(max_depth=0, max_leaves=255, n_estimators=num_rounds, min_child_weight=30, learning_rate=0.1, scale_pos_weight=2, gamma=0.1, reg_lambda=1, subsample=1, grow_policy='lossguide', tree_method='hist', n_jobs=-1, random_state=77) lgbm_clf_pipeline = lgb.LGBMRegressor(num_leaves=255, n_estimators=num_rounds, min_child_weight=30, learning_rate=0.1, scale_pos_weight=2, min_split_gain=0.1, reg_lambda=1, subsample=1, n_jobs=-1, seed=77)
I did this experiment 5 years ago with the same parameters as now, and the 3 algorithms had similar performance. The performance drop could be because there has been changes in the parameters or because there is a bug.
Any idea why this could be happening?