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When I want to use my model:
```
gb = GradientBoostingClassifier(n_estimators=500, max_depth=10, learning_rate=0.01)
rf = RuleFit(gb)
rf.fit(X_train.as_matrix(), y_train.values, feature_names=fea…
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I would absolutely love to be able to use this package - Unfortunately I have know idea how to call any of the seemingly useful R files in this repo. I would love to see some example where, in R, one …
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we could add “_pretrained_tree_models” parameter to RuleFit - this would let user specify one or more tree models where to extract rules from and then proceed the same way as with regular rulefit
we c…
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Currently, there are two options “DRF”, or “GBM” as possible values to assign to the algorithm parameter for Rulefit. Adding XGBoost as another option to the algorithm parameter would be great.
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Exception:
java.lang.NullPointerException
at java.util.ArrayList.addAll(ArrayList.java:581)
at hex.rulefit.RuleFitMojoWriter.writeOrderedRuleEnsemble(RuleFitMojoWriter.java:101)
at hex.rulefit.RuleFit…
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Rulefit coefficients are misleading/confusing if the target is not a 0/1 binary. For example, if I am predicting "LOW"/"HI" what do the coefficients mean?
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The current implementation of `rulefit` can sometimes produce redundant features that are then fed into the lasso. This comes from the stochastic nature of random trees and lack of rule pruning.
To…
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reproducible by
{code:java}
final Frame fr = Scope.track(parseTestFile("missing.csv"));
RuleFitModel.RuleFitParameters params = new RuleFitModel.RuleFitParameters();
…