Comments (1)
Thank you for using KTBoost!
Yes, this is possible. You basically have to add the option to use arrays instead of numbers for yl
and yu
in the TobitLossFunction
. I currently don't have time to work on this. Contributions are welcome.
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Related Issues (10)
- Method - update_terminal_regions in LossFunction Class- If Condition HOT 4
- 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
- Implement non-constant learning rates HOT 1
- sample_weight is being multiplied twice - Tobit Loss HOT 3
- Is it possible to add a monotone constraint? HOT 1
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