Comments (4)
Hi @Soumyatifr , interesting case. Can't guess the reason, but for sanity check - how about leaving only one variable ('HT') in training and see if it gets corrected?
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Dear Alex @arogozhnikov,
Thanks for your very prompt reply, I have checked with only "HT" variable but still it doesn't want, it would be very hgelpful for me, if you can see the code [1] and the two root files , which I have uploaded to my google drive[2].
[1] https://github.com/Soumyatifr/GBReweighting/blob/main/bdt_reweighet.ipynb
[2] https://drive.google.com/drive/folders/1ZCkF0V58O_fC1gnKqFRL-HYaE97mYwbe?usp=sharing
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@Soumyatifr
just ran your code with single HT variable, and it clearly has strong improvements:
hopefully it looks similar in your env, otherwise there is some problem with package versions.
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ok, assume that helped
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Related Issues (20)
- Random behavior of GBReweighter and UGradientBoostingClassifier
- Nominal weights when correcting already weighted original HOT 1
- Assertion Error with UGradientBoost HOT 1
- sPlot returns NAN sWeights HOT 3
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- Using sWeights with GBReweighter HOT 1
- Saving uboost BDT with tf/keras base estimators HOT 5
- Persistify GBReweighter instance HOT 1
- Error propagation from weights HOT 6
- Create a new release? HOT 1
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- Large variations in signal/background distributions HOT 7
- GBReweighter KeyError: 'squared_error' ?? HOT 7
- Porting loss function to XGBoost HOT 1
- numpy.float and numpy.int deprecated/removed in newer versions of numpy HOT 3
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