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.


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install.packages("mlr3pipelines")

0.1.3 by Martin Binder, 4 months ago


https://mlr3pipelines.mlr-org.com, https://github.com/mlr-org/mlr3pipelines


Report a bug at https://github.com/mlr-org/mlr3pipelines/issues


Browse source code at https://github.com/cran/mlr3pipelines


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


Documentation:   PDF Manual  


LGPL-3 license


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

Suggests ggplot2, glmnet, igraph, knitr, lgr, lme4, mlbench, mlr3filters, mlr3learners, mlr3measures, nloptr, rmarkdown, rpart, testthat, visNetwork, bestNormalize, fastICA, kernlab, smotefamily, evaluate


Imported by mlr3proba.

Depended on by mlr3verse.

Suggested by mlr3tuning.


See at CRAN