Preprocessing Operators and Pipelines for 'mlr3'

Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.


Reference manual

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0.3.0 by Martin Binder, a month ago,

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Browse source code at

Authors: Martin Binder [aut, cre] , Florian Pfisterer [aut] , Lennart Schneider [aut] , Bernd Bischl [aut] , Michel Lang [aut] , Susanne Dandl [aut]

Documentation:   PDF Manual  

LGPL-3 license

Imports backports, checkmate, data.table, digest, lgr, mlr3, mlr3misc, paradox, R6, withr

Suggests bibtex, ggplot2, glmnet, igraph, knitr, lme4, mlbench, bbotk, mlr3filters, mlr3learners, mlr3measures, nloptr, quanteda, rmarkdown, rpart, stopwords, testthat, visNetwork, bestNormalize, fastICA, kernlab, smotefamily, evaluate, NMF, MASS, kknn, GenSA, methods, vtreat

Imported by mlr3fselect.

Depended on by mlr3verse.

Suggested by mlr3proba, mlr3tuning.

See at CRAN