Analyze the practical relevance importance of GBT hyperparameters #33137
Description
The following paper ran many hyper-parameter tuning experiments.
The raw CSV data is available for download here:
We could try to mine this data to find hyper-params configuration implemented in xgboost or lightgbm that:
- allow xgboost or lightgbm or catboost to be on the Pareto optimal frontier of the predictive/computational performance tradeoff;
- have no equivalent in scikit-learn.
This would help identify which missing entries of the table in #27873 have the most user-facing impact, and possibly also identify features of xgboost that are not implemented in scikit-learn at all while being very relevant to reach good predictive performance.