Comments (1)
Sorry for the super long delay and thanks for your interest 😄 ! We have been working to unify all the models into a solid base before dealing with details of individual models.
Bayesian Rule List is a very flexible model and this implementation only allows control over the parameters you listed above. In this case, the listwidth prior is the parameter that you most want to vary (the lower it is, the less rules). Limiting maxcardinality will help you get shorter rules. In either case, the algorithm will try to find the best list based on these hyperparameters!
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Related Issues (20)
- 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
- No function to extract rules from HSTreeClassifierCV
- HSTree 'works' without error with unsupported sklearn models, but doesn't do anything
- RuleFit parameter `sample_fract` seems to do nothing
- How to calculate shapley values for HSTreeClassifier
- FIGS with Multiclass Classification
- model.plot() doesn't work HOT 1
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