Comments (1)
csinva
commented on May 13, 2024
Thanks for your interest in the library! Sorry about that - we hadn't been fully supporting FIGSRegressorCV, since best practice is likely to wrap the standard FIGSRegressor with sklearn's cross-validation methods.
I just updated imodels to support it though, so if you run pip install --upgrade imodels, you should be able to import FIGSRegressorCV!
from imodels.
Related Issues (20)
- Link to Skope Rules in README brings elsewhere
- Possible bugs in GreedyRuleListClassifier HOT 3
- Rules list cutoffs are not printed in string representations of GreedyRulesListClassifier HOT 2
- GreedyRulesListClassifier rules don't describe points in all decision regions in some cases HOT 2
- Is library supporting scikit-learn API such as XGBoost or LightGBM compatible to HSTreeClassifier/Regressor?
- Inconsistent types for RuleFit._extract_rules() HOT 1
- HSTree LOOCV Speed-up
- BoostedRulesClassifier, BoostedRulesRegressor not working with sklearn cross_validate HOT 3
- FIGSRegressor failed to calculate importance_data_tree
- 'FIGS' object has no attribute 'decision_function' HOT 1
- DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future HOT 1
- Problem importing functions from module `imodels.importance` HOT 2
- Bugs about fit SLIMClassifier, BayesianRuleSetClassifier, SlipperClassifier, TaoTreeRegressor
- How to extract rules from FIGSClassifier ? HOT 2
- How to implement the importance of variable and rule in the RuleFitClassifier algorithm?
- HSTreeClassifierCV ignore the feature name HOT 1
- How to extract rules from BoostedRulesClassifier?
- Trouble with FIGS
- No support for class_weight = "balanced" in FIGSClassifier
- Updation in colab notebook
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from imodels.