R package CORElearn: Classification, regression, feature evaluation and ordinal evaluation. CORElearn is machine learning suite ported to R from standalone C++ package. It contains several model learning techniques in classification and regression, for example classification and regression trees with optional constructive induction and models in the leafs, random forests, kNN, naive Bayes, and locally weighted regression. It is especially strong in feature evaluation where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM... Its additional strength is ordEval algorithm and its visualization used for evaluation of data sets with ordinal features and class. Several algorithms support parallel multithreaded execution via OpenMP. The top level documentation is reachable through ?CORElearn.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
- Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
- Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: mlr: machine learning in (\mathbfR) (2016)
- Lala Riza; Christoph Bergmeir; Francisco Herrera; José Benítez: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R (2015) not zbMATH
- Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)