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  "Description": "An implementation of the RuleFit algorithm as described in\nFriedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme\nGradient Boosting ('XGBoost') is used to build rules, and\n'glmnet' is used to fit a sparse linear model on the raw and\nrule features. The result is a model that learns similarly to a\ntree ensemble, while often offering improved interpretability\nand achieving improved scoring runtime in live applications.\nSeveral algorithms for reducing rule complexity are provided,\nmost notably hyperrectangle de-overlapping. All algorithms\nscale to several million rows and support sparse\nrepresentations to handle tens of thousands of dimensions.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/holub008/xrf",
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