Efficient, object-oriented programming on the building blocks of machine learning. Successor of mlr.
remotes::install_github("mlr-org/mlr3")
library(mlr3) set.seed(1) # create learning task task_iris = TaskClassif$new(id = "iris", backend = iris, target = "Species") task_iris # load learner and set hyperparamter learner = lrn("classif.rpart", cp = 0.01)
# train/test split train_set = sample(task_iris$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$confusion measure = msr("classif.acc") prediction$score(measure)
# automatic resampling resampling = rsmp("cv", folds = 3L) rr = resample(task_iris, learner, resampling) rr$score(measure) rr$aggregate(measure)
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.
mlr
nicely.data.table
for fast and convenient data frame computations.data.table
and R6
, for this we will make heavy use of list columns in data.tables.mlr3
requires the following packages:backports
: Ensures backward compatibility with older R releases. Developed by members of the mlr
team. No recursive dependencies.checkmate
: Fast argument checks. Developed by members of the mlr
team. No extra recursive dependencies.mlr3misc
Miscellaneous functions used in multiple mlr3 extension packages. Developed by the mlr
team. No extra recursive dependencies.paradox
: Descriptions for parameters and parameter sets. Developed by the mlr
team. No extra recursive dependencies.R6
: Reference class objects. No recursive dependencies.data.table
: Extension of R's data.frame
. No recursive dependencies.digest
: Hash digests. No recursive dependencies.lgr
: Logging facility. No extra recursive dependencies.Metrics
: Package which implements performance measures. No recursive dependencies.mlbench
: A collection of machine learning data sets. No dependencies.mlr3
utilizes the future
and future.apply
packages.evaluate
and callr
can be used.mlr-outreach holds all outreach activities related to mlr and mlr3.
mlr3 talk at useR! 2019 conference in Toulouse, France:
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