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mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines.
Topçuoglu, Begüm D; Lapp, Zena; Sovacool, Kelly L; Snitkin, Evan; Wiens, Jenna; Schloss, Patrick D.
Afiliação
  • Topçuoglu BD; Department of Microbiology & Immunology, University of Michigan.
  • Lapp Z; Exploratory Science Center, Merck & Co., Inc., Cambridge, Massachusetts, USA.
  • Sovacool KL; Department of Computational Medicine & Bioinformatics, University of Michigan.
  • Snitkin E; Department of Computational Medicine & Bioinformatics, University of Michigan.
  • Wiens J; Department of Microbiology & Immunology, University of Michigan.
  • Schloss PD; Department of Internal Medicine/Division of Infectious Diseases, University of Michigan.
Article em En | MEDLINE | ID: mdl-34414351
ABSTRACT
Machine learning (ML) for classification and prediction based on a set of features is used to make decisions in healthcare, economics, criminal justice and more. However, implementing an ML pipeline including preprocessing, model selection, and evaluation can be time-consuming, confusing, and difficult. Here, we present mikropml (prononced "meek-ROPE em el"), an easy-to-use R package that implements ML pipelines using regression, support vector machines, decision trees, random forest, or gradient-boosted trees. The package is available on GitHub, CRAN, and conda.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article