Incorrect AUC value in CatBoost chart with sample_weight

[offchan42]

I trained a dataset with sample weight using 3 algos: LightGBM, Xgboost, and CatBoost. I found that the learning curve chart for CatBoost doesn't take into account the sample weight but the score in the table does. Maybe you forgot to put sample_weight for CatBoost charts? I also see the problems in the ROC curve chart (but it's the same behavior among all models). Also, could this affect the training result e.g. terminating at the wrong place? Because I saw the model trained for many iterations.

[pplonski]

@off99555 thank you for reporting. Is it the problem only for CatBoost?

[offchan42]

@pplonski Yes, I think so. At least LightGBM and Xgboost don't seem to have this problem (wrong metric in the learning curve chart).

[alencn1024]

kindly to ask is there somebody working on this issue? If not, I'm glad to undertake it @pplonski

[pplonski]

@alencn1024 thanks for looking into it!