SIRUS grows a random forest whose splits are limited to fixed quantiles, counts how often each rule appears across the forest, and retains only the most frequent rules to produce a stable and readable classifier.
Fitting Prediction Rule Ensembles with R Package pre
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abstract
Prediction rule ensembles (PREs) are sparse collections of rules, offering highly interpretable regression and classification models. This paper presents the R package pre, which derives PREs through the methodology of Friedman and Popescu (2008). The implementation and functionality of package pre is described and illustrated through application on a dataset on the prediction of depression. Furthermore, accuracy and sparsity of PREs is compared with that of single trees, random forest and lasso regression in four benchmark datasets. Results indicate that pre derives ensembles with predictive accuracy comparable to that of random forests, while using a smaller number of variables for prediction.
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SIRUS: Stable and Interpretable RUle Set for Classification
SIRUS grows a random forest whose splits are limited to fixed quantiles, counts how often each rule appears across the forest, and retains only the most frequent rules to produce a stable and readable classifier.