Comments (1)
I guess that this is simply due to the fact that building regression trees with the 'mae' criterion is slower than with the other ones ('mse' or 'friedman_mse') for which there are fast version. I must admit though, that I am not an expert concerning regression trees with the MAE criterion.
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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
- 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
- Compatibility with scikit-learn 0.24.0 HOT 4
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