c(person(given = "Michel",
family = "Lang",
role = c("cre", "aut"),
email = "michellang@gmail.com",
comment = c(ORCID = "0000-0001-9754-0393")),
person(given = "Bernd",
family = "Bischl",
role = "aut",
email = "bernd_bischl@gmx.net",
comment = c(ORCID = "0000-0001-6002-6980")),
person(given = "Jakob",
family = "Richter",
role = "aut",
email = "jakob1richter@gmail.com",
comment = c(ORCID = "0000-0003-4481-5554")),
person(given = "Patrick",
family = "Schratz",
role = "aut",
email = "patrick.schratz@gmail.com",
comment = c(ORCID = "0000-0003-0748-6624")),
person(given = "Giuseppe",
family = "Casalicchio",
role = "ctb",
email = "giuseppe.casalicchio@stat.uni-muenchen.de",
comment = c(ORCID = "0000-0001-5324-5966")),
person(given = "Stefan",
family = "Coors",
role = "ctb",
email = "mail@stefancoors.de",
comment = c(ORCID = "0000-0002-7465-2146")),
person(given = "Quay",
family = "Au",
role = "ctb",
email = "quayau@gmail.com",
comment = c(ORCID = "0000-0002-5252-8902")),
person(given = "Martin",
family = "Binder",
role = "aut",
email = "mlr.developer@mb706.com"))
Efficient, object-oriented programming on the building blocks of
machine learning. Provides 'R6' objects for tasks, learners, resamplings,
and measures. The package is geared towards scalability and larger datasets
by supporting parallelization and out-of-memory data-backends like
databases. While 'mlr3' focuses on the core computational operations,
add-on packages provide additional functionality.
backports, checkmate (>= 1.9.3), data.table, digest, lgr (>=
0.3.0), Metrics, mlbench, mlr3misc (>= 0.1.1), paradox, R6
callr, datasets, devtools, evaluate, future (>= 1.9.0),
future.apply (>= 1.1.0), future.callr, Matrix, rpart, testthat,
titanic
'mlr_reflections.R' 'BenchmarkResult.R' 'DataBackend.R'
'DataBackendCbind.R' 'DataBackendDataTable.R'
'DataBackendMatrix.R' 'DataBackendRbind.R' 'Generator.R'
'mlr_generators.R' 'Generator2DNormals.R'
'GeneratorFriedman1.R' 'GeneratorSmiley.R' 'GeneratorXor.R'
'Learner.R' 'LearnerClassif.R' 'mlr_learners.R'
'LearnerClassifDebug.R' 'LearnerClassifFeatureless.R'
'LearnerClassifRpart.R' 'LearnerRegr.R'
'LearnerRegrFeatureless.R' 'LearnerRegrRpart.R' 'Measure.R'
'MeasureClassif.R' 'mlr_measures.R' 'MeasureClassifACC.R'
'MeasureClassifAUC.R' 'MeasureClassifCE.R'
'MeasureClassifConfusion.R' 'MeasureClassifCosts.R'
'MeasureClassifF1.R' 'MeasureElapsedTime.R' 'MeasureOOBError.R'
'MeasureRegr.R' 'MeasureRegrMAE.R' 'MeasureRegrMSE.R'
'MeasureRegrRMSE.R' 'MeasureSelectedFeatures.R' 'Prediction.R'
'PredictionClassif.R' 'PredictionRegr.R' 'ResampleResult.R'
'Resampling.R' 'mlr_resamplings.R' 'ResamplingBootstrap.R'
'ResamplingCV.R' 'ResamplingCustom.R' 'ResamplingHoldout.R'
'ResamplingRepeatedCV.R' 'ResamplingSubsampling.R' 'Task.R'
'TaskSupervised.R' 'TaskClassif.R' 'mlr_tasks.R'
'TaskClassif_german_credit.R' 'TaskClassif_iris.R'
'TaskClassif_pima.R' 'TaskClassif_sonar.R' 'TaskClassif_spam.R'
'TaskClassif_wine.R' 'TaskClassif_zoo.R' 'TaskRegr.R'
'TaskRegr_boston_housing.R' 'TaskRegr_mtcars.R'
'Task_mutators.R' 'as_data_backend.R' 'assertions.R'
'benchmark.R' 'cast_from_dict.R' 'expand_grid.R'
'helper-parallelization.R' 'helper.R' 'mlr_control.R'
'reexports.R' 'resample.R' 'worker.R' 'zzz.R'