Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

Documentation

Manual: mlr.pdf
Vignette: mlr

Maintainer: Bernd Bischl <bernd_bischl at gmx.net>

Author(s): Bernd Bischl*, Michel Lang*, Lars Kotthoff*, Julia Schiffner*, Jakob Richter*, Zachary Jones*, Giuseppe Casalicchio*, Mason Gallo*, Jakob Bossek*, Erich Studerus*, Leonard Judt*, Tobias Kuehn*, Pascal Kerschke*, Florian Fendt*, Philipp Probst*, Xudong Sun*, Janek Thomas*, Bruno Vieira*, Laura Beggel*, Quay Au*, Martin Binder*, Florian Pfisterer*, Stefan Coors*

Install package and any missing dependencies by running this line in your R console:

install.packages("mlr")

Reverse
depends
llama, mlrMBO, OOBCurve, OpenML, RBPcurve, unbalanced
Reverse
imports
aslib, flacco
Reverse
suggests
Reverse
enhances
liquidSVM
Reverse
linking to

Package mlr
Materials
URL https://github.com/mlr-org/mlr
Task Views MachineLearning
Version 2.11
Published 2017-03-15
License BSD_2_clause + file LICENSE
BugReports https://github.com/mlr-org/mlr/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks mlr check results
Package source mlr_2.11.tar.gz