mlr3

Package website: release | dev

Efficient, object-oriented programming on the building blocks of machine learning. Successor of mlr.

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Installation

Install the last release from CRAN:

install.packages("mlr3")

Install the development version from GitHub:

remotes::install_github("mlr-org/mlr3")

Example

Constructing Learners and Tasks

library(mlr3)

# create learning task
task_iris = TaskClassif$new(id = "iris", backend = iris, target = "Species")
task_iris
## <TaskClassif:iris> (150 x 5)
## * Target: Species
## * Properties: multiclass
## * Features (4):
##   - dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
# load learner and set hyperparamter
learner = lrn("classif.rpart", cp = 0.01)

Basic train + predict

# train/test split
train_set = sample(task_iris$nrow, 0.8 * task_iris$nrow)
test_set = setdiff(seq_len(task_iris$nrow), train_set)

# train the model
learner$train(task_iris, row_ids = train_set)

# predict data
prediction = learner$predict(task_iris, row_ids = test_set)

# calculate performance
prediction$confusion
##             truth
## response     setosa versicolor virginica
##   setosa         11          0         0
##   versicolor      0         12         1
##   virginica       0          0         6
measure = msr("classif.acc")
prediction$score(measure)
## classif.acc 
##   0.9666667

Resample

# automatic resampling
resampling = rsmp("cv", folds = 3L)
rr = resample(task_iris, learner, resampling)
rr$score(measure)
##             task task_id               learner    learner_id     resampling
## 1: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
## 2: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
## 3: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
##    resampling_id iteration prediction classif.acc
## 1:            cv         1     <list>        0.92
## 2:            cv         2     <list>        0.92
## 3:            cv         3     <list>        0.94
rr$aggregate(measure)
## classif.acc 
##   0.9266667

Why a rewrite?

mlr was first released to CRAN in 2013. Its core design and architecture date back even further. The addition of many features has led to a feature creep which makes mlr hard to maintain and hard to extend. We also think that while mlr was nicely extensible in some parts (learners, measures, etc.), other parts were less easy to extend from the outside. Also, many helpful R libraries did not exist at the time mlr was created, and their inclusion would result in non-trivial API changes.

Design principles

Extension Packages

Consult the wiki for short descriptions and links to the respective repositories.

Contributing to mlr3

This R package is licensed under the LGPL-3. If you encounter problems using this software (lack of documentation, misleading or wrong documentation, unexpected behaviour, bugs, …) or just want to suggest features, please open an issue in the issue tracker. Pull requests are welcome and will be included at the discretion of the maintainers.

Please consult the wiki for a style guide, a roxygen guide and a pull request guide.

Citing mlr3

If you use mlr3, please cite our JOSS article:

@Article{mlr3,
  title = {{mlr3}: A modern object-oriented machine learning framework in {R}},
  author = {Michel Lang and Martin Binder and Jakob Richter and Patrick Schratz and Florian Pfisterer and Stefan Coors and Quay Au and Giuseppe Casalicchio and Lars Kotthoff and Bernd Bischl},
  journal = {Journal of Open Source Software},
  year = {2019},
  month = {dec},
  doi = {10.21105/joss.01903},
  url = {https://joss.theoj.org/papers/10.21105/joss.01903},
}