cerlymarco/shap-hypetune
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
Jupyter NotebookMIT
Issues
- 1
[Feature Request] Support simple base learners such as DecisionTreeRegressor/Classifier
#39 opened by jolespin - 1
Specify versions of dependencies
#38 opened by jolespin - 1
- 1
- 3
- 3
How to get the exact SHAP importance values?
#33 opened by wishforgood - 1
- 2
Issue with hyperopt
#28 opened by MenaWANG - 1
Possible to use hyperopts q-distributions?
#26 opened by samisomu - 4
- 0
- 1
Question about SHAP importance
#24 opened by loveis98 - 5
- 11
Eval Metric directionality?
#21 opened by ericvoots - 1
K-fold or Blocked cross validation
#20 opened by ericvoots - 4
Suppress warnings
#19 opened by Rane90 - 3
ExplainerError
#18 opened by hasan-sayeed - 4
- 1
Erratic behaviour
#16 opened by mirix - 2
- 1
Issue with custom scorer
#14 opened by mirix - 0
Any plan to write a publication or preprint.
#13 opened by JiaxiangBU - 1
Error in BoostBoruta
#12 opened by zeydabadi - 2
- 2
List of the important features?
#4 opened by jmrichardson - 1
only 10 features show in the BoostBoruta, without any feature labels/ranks/indexes
#9 opened by raqueldias - 1
Callbacks for progress tracking
#8 opened by tautrimas - 1
Any plan to support catboost estimator?
#7 opened by ogencoglu - 2
- 1
Combining with original XGboost
#5 opened by lifeodyssey - 1
- 2
- 3