add “_pretrained_tree_models” parameter to RuleFit

h2oai / h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

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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 could add a convenience function on estimators to call this and produce the “simplified” model

so if there is this parameter specified, then there will be no tree training part but algo will directly jump to extracting rules from provided models.

update:

_pretrained_tree_models should accept model keys or grid search keys - analogically as stacked ensemble is doing in here [https://github.com/h2oai/h2o-3/blob/master/h2o-algos/src/main/java/hex/ensemble/StackedEnsemble.java#L115|https://github.com/h2oai/h2o-3/blob/master/h2o-algos/src/main/java/hex/ensemble/StackedEnsemble.java#L115]

h2oai / h2o-3

add “_pretrained_tree_models” parameter to RuleFit #7556