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2016 JMLR JMLR 2016

mlr: Machine Learning in R

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., hyperparameter 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. [abs] [ pdf ][ bib ] © JMLR 2016. (edit, beta)

📈 Trend Setter — Model Merging
🧭 Keyword Pioneer — machine learning framework
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
🌉 Interdisciplinary Bridge — Computer Science and Machine Learning
🐣 Hot Topic Early Bird — model selection

Authors

Bernd Bischl , Michel Lang , Lars Kotthoff , Julia Schiffner , Jakob Richter , Erich Studerus , Giuseppe Casalicchio , Zachary M. Jones

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Clustering Machine Learning > Application Areas > Model Merging Computer Science > Applications > Software Engineering Machine Learning > Learning Types > Ensemble Learning

Keywords

model selection feature selection machine learning hyperparameter tuning machine learning framework ensemble construction r language
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