{ "_id": "6a103302acfb0bcc41c97fed", "Package": "CORElearn", "Title": "Classification, Regression and Feature Evaluation", "Version": "1.57.3.1", "Date": "2022-11-18", "Authors@R": "c(person(given = \"Marko\",\nfamily = \"Robnik-Sikonja\",\nrole = c(\"aut\", \"cre\"),\nemail = \"marko.robnik@fri.uni-lj.si\"),\nperson(given = \"Petr\",\nfamily = \"Savicky\",\nrole = \"aut\"))", "Description": "A suite of machine learning algorithms written in C++ with\nthe R interface contains several learning techniques for\nclassification and regression. Predictive models include e.g.,\nclassification and regression trees with optional constructive\ninduction and models in the leaves, random forests, kNN, naive\nBayes, and locally weighted regression. All predictions\nobtained with these models can be explained and visualized with\nthe 'ExplainPrediction' package. This package is especially\nstrong in feature evaluation where it contains several variants\nof Relief algorithm and many impurity based attribute\nevaluation functions, e.g., Gini, information gain, MDL, and\nDKM. These methods can be used for feature selection or\ndiscretization of numeric attributes. The OrdEval algorithm and\nits visualization is used for evaluation of data sets with\nordinal features and class, enabling analysis according to the\nKano model of customer satisfaction. Several algorithms support\nparallel multithreaded execution via OpenMP. 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