Comments (2)
Hey, Alex.
I looked through your notebook, and everything is correct there.
I tried to find where this computation is done in the code, but I couldn't find it.
It's a bit tricky, there is a way to substitute optimizing chi^2 with minimizing MSE (and found splits will be identical).
Given that a) most packages are able to optimize MSE out-of-the-box b) I don't want to write one more tree, hep_ml
makes use of this trick. In particual, ReweightLossFunction is constructing appropriate X, y
parameters for tree to minimize MSE.
(And, of course, thanks for the excellent package!
🍻 )
Thanks, you're welcome :)
from hep_ml.
Ah neat, that makes sense. That's for the explanation!
from hep_ml.
Related Issues (20)
- Negative sWeights HOT 8
- uBoost Convergence HOT 1
- Multidimensional reweighting HOT 2
- 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
- Odd behaviour of GBReweighter HOT 3
- 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
- Theano is going away HOT 1
- Benchmark with independent classification model HOT 3
- New release? HOT 2
- Large variations in signal/background distributions HOT 7
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- 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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