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Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jajob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M. (2016): mlr: Machine Learning in R. In: Journal of Machine Learning Research, Vol. 17, 1
Full text not available from 'Open Access LMU'.

Abstract

The MLR package provides a generic, object-oriented, and extensible framework for classification, regression, survival analysis and clustering for the R language. It provides a unified interface to more than 160 basic learners and includes meta-algorithms and model selection techniques to improve and extend the functionality of basic learners with, e.g., hyperpa-rameter tuning, feature selection, and ensemble construction. Parallel high-performance computing is natively supported. The package targets practitioners who want to quickly apply machine learning algorithms, as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment.