Comments (1)
Thank you for bringing this up, Yu! This would for sure be helpful for credit scoring and also other areas.
Given that the solution done by XGBoost and LightGBM is relatively simple (don't allow for splits that are not in line with the constraints, as far as I remember...), this can for sure be implemented. Note that this is also being discussed for scikit-learn, see e.g. scikit-learn/scikit-learn#6656. I.e., we can likely use code / ideas from there.
I currently don't have time to work on this, but any contributions are welcome.
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Related Issues (10)
- Method - update_terminal_regions in LossFunction Class- If Condition HOT 4
- Tobit with yl and yu varied by observations HOT 1
- KTBoost.BoostingRegressor TypeError: __cinit__() takes exactly 6 positional arguments (7 given) HOT 1
- Multiprocessing with KTBoost
- mae criterion is very slow compared to mse or friedman_mse for classification HOT 1
- TypeError: __init__() got an unexpected keyword argument 'min_weight_leaf' HOT 2
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- sample_weight is being multiplied twice - Tobit Loss HOT 3
- Compatibility with scikit-learn 0.24.0 HOT 4
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