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Changelog

Source: NEWS.md

mlr3extralearners 1.2.0

New Features

  • New Learners:
    • LearnerCompRisksRandomForestSRC
    • LearnerSurvBlockForest
    • Learner{Classif,Regr,Surv}BlockForest
    • Learner{Classif,Regr}ExhaustiveSearch
    • LearnerClassifFastai
    • Learner{Classif,Regr}Penalized
    • Learner{Classif,Regr}Bst
    • LearnerClassifAdabag
    • LearnerClassifAdaBoosting
    • Learner{Classif,Regr}Evtree
    • LearnerClassifKnn
    • LearnerClassifRotationForest
    • LearnerRegrCrs
    • LearnerClassifStepPlr
    • LearnerClassifMda
    • LearnerClassifRferns
    • LearnerClassifNeuralnet
    • LearnerRegrBrnn
    • LearnerRegrBotorchSingleTaskGP
    • LearnerRegrBotorchMixedSingleTaskGP
  • Add new control_custom_fun parameter in surv.aorsf
  • New function learner_is_runnable() to check whether the required packages to train a learner are available.
  • Added selected_features property to RandomForestSRC learners (prediction doesn’t work if vars.used = 'all.trees')

Bug fixes

  • Tests are now skipped when the suggested packages is not available. This will make local development much more convenient.
  • Removed parameters from RandomForestSRC learners that weren’t used + optimized tests
  • Removed discrete parameter from surv.parametric, so that it is impossible to return distr6::VectorDistribution survival predictions (softly deprecated in mlr3proba@v0.8.1)

Breaking Changes

  • All (extra) density learners are removed. These will be transferred to mlr3proba soon (see v0.8.2 or later).
  • The create_learner() generator was removed, because it was hard to maintain and boilerplate code in the age of LLMs is easy enough to write.
  • remove discrete parameter from surv.parametric, so that it is impossible to return distr6::VectorDistribution survival predictions (softly deprecated in mlr3proba@v0.8.1)
  • classif.lightgbm now works with encapsulation with multiclass tasks
  • the package no longer re-exports lrn and lrns, which should anyway be available to the user as the package depends on mlr3, where these functions are defined.
  • Removed various learners:
    • randomPlantedForest was removed, because there is currently no way to save the model.
    • The deep learning methods from survivalmodels were removed, because they also cannot be saved and because the upstream package is archived.

Other

  • The package now imports withr
  • mlr3proba is now an import and no longer a suggested package.
  • mlr3cmprsk is added as an import.
  • The package no longer uses set.seed() in the tests and instead uses withr::local_seed() This means the auto tests will be stochastic like they should be.
  • The CI now checks that RCMD-check passes when suggested packages are not available.
  • distr6 dependency is removed. partykit survival learners use constant interpolation of the predicted Kaplan-Meier curves via survdistr::vec_interp()

mlr3extralearners 1.1.0

New Features:

  • Support offset in learners regr|classif.mgcv, regr.glm and regr.lmer.
  • Added learners LearnerRegrQGam and LearnerRegrMQGam.
  • Added learners LearnerClassifTabPFN and LearnerRegrTabPFN.
  • Added the new version of learner weights to all learners that support weights
  • Added marshaling for surv.xgboost.cox.
  • Added learner LearnerClassifKnn.

Bugfixes:

  • lightgbm classifier now works with encapsulation (#437)

mlr3extralearners 1.0.0

  • Add “Prediction types” doc section for all 30 survival learners + make sure it is consistent #347
  • All survival learners have crank as main prediction type (and it is always returned) #331
  • Added minimum working version for all survival learners in DESCRIPTION file
  • Harmonized the use of times points for prediction as much as possible across survival learners #387
    • added gridify_times() function to coarse time points
    • fixed surv.parametric and surv.akritas use of ntime argument
  • surv.parametric is now used by default with discrete = TRUE (no survival learner returns now distr6 vectorized distribution by default)
  • Doc update for mlr3 (version 0.21.0)
  • Fixed custom and initial values across all learners documentation pages
  • Fixed doc examples that used learner$importance()
  • Set n_thread = 1 for surv.aorsf and use unique event time points for predicted S(t)
  • Add selected_features() for surv.penalized
  • Fix surv.prioritylasso learner + add distr predictions via Breslow #344
  • Survival SVM gamma.mu parameter was split to gamma and mu to enable easier tuning (surv.svm learner)

mlr3extralearners 0.9.0

  • Added response (i.e., survival time) prediction to aorsf learner
  • Updated support for flexsurv v2.3
  • Fixed bug in catboost that caused invalid probability levels during resample() or benchmark() (#353)
  • the $model slot of lrn("classif.abess") now contains the model of the upstream package again.
  • Add early stopping and validation support to learners lrn("surv.xgboost.aft") and lrn("surv.xgboost.cox").
  • Added early stopping and validation to catboost and lightgbm.
  • Added missing case.depth parameter to rfsrc learners.
  • mlr3 is now in Depends instead of Imports.
  • Deprecated learner lrn("surv.xgboost") was now removed. Use lrn("surv.xgboost.cox") or lrn("surv.xgboost.aft") instead.
  • Change xgboost default nrounds from 1 to 1000.
  • remove obliqueRSF Learner which was long superseded by aorsf
  • a lot of examples were added to the learners

mlr3extralearners 0.8.0

  • Added surv.xgboost.cox and surv.xgboost.aft separate survival learners. distr prediction on the cox xgboost learner is now estimated via Breslow by default and aft xgboost has now in addition a response prediction (survival time)
  • Ported surv.parametric code to survivalmodels, changed type parameter to form to avoid conflict with survivalmodels’s default parameter list
  • Fix: Replace hardcoded VectorDistributions from partykit and flexsurv survival learners with survival matrices (Matdist) (thanks to @bblodfon)
  • Feat: Add discrete parameter in surv.parametric learner to return Matdist survival predictions
  • Added method selected_features() to CoxBoost survival learners (thanks to @bblodfon)
  • Added the Random Planted Forest Learner (thanks to @jemus42)
  • re-added the catboost learner as it was requested (was previously removed because of installation issues)
  • surv.ranger now receives parameters during $predict() (thanks to @jemus42)
  • Feature: Learner surv.bart was added (thanks to @bblodfon)
  • Parameters of lrn("surv.aorsf") were updated (thanks to @bcjaeger)
  • Various minor doc improvements
  • Added the distr predict type to the surv.cv_glmnet and surv.glmnet learners (thanks to @bblodfon)
  • Feat: Added many new WEKA learners (thanks to @damirpolat)
  • Fix: I and F params from IBk learner are too interdependent (I can only be TRUE when F is FALSE and vice versa). Combined them into one factor param weight that has two levels – I and F.
  • Fix: U must be FALSE for S to be tunable in J48 learner.
  • Compatibility with upcoming ‘paradox’ release.

mlr3extralearners 0.7.1

  • Add parameter perf.type to rfsrc learners
  • Add vignette about “extending learners” which was previously in the mlr3book.
  • Remove the "multiclass" property from lrn("classif.gbm"), as this feature is broken.

mlr3extralearners 0.7.0

  • Add new parameters to lightgbm learners
  • Add feature type "factor" to gam learners
  • Add new parameter min.bucket to ranger
  • Remove catboost learner (because the developers don’t properly take care of the R package)
  • Add argument nthreads to dbarts learners; set verbose to FALSE by default (thanks to @ck37)
  • Add new parameters to prioritylasso
  • Fix: available levels for parameter of imbalanced random forest (typo)

mlr3extralearners 0.6.1

  • BREAKING CHANGE: lightgbm’s early stopping mechanism now uses the task’s test set.
  • feat: Add two new learners regr.abess and classif.abess (thanks to @bbayukari)
  • feat: Added learner LearnerClassifImbalancedRandomForestSRC (thanks to
  • Feat: Added learners LearnerClassifPriorityLasso, LearnerRegrPriorityLasso, LearnerSurvPriorityLasso (thanks to

mlr3extralearners 0.6.0

  • Feat: Added learner LearnerClassifGlmer (https://github.com/mlr-org/mlr3extralearners/issues/243)
  • Fix: Failing xgboost parameter test
  • Fix: Add arguments nei and ncv.thread that were added to mgcv::gam() in version 1.8.41
  • Fix: Added missing property "weights" to LearnerClassifGlmer and LearnerRegrLmer
  • Fix: lightgbm uses the param_vals stored in the state for hotstarting
  • Fix: Rely on state$data_prototype to get ordering of features via ordered_features() like in mlr3learners and therefore obviate the need to store feature_names in the state
  • Fix: extralearners are removed from mlr_learners when unloading mlr3extralearners

mlr3extralearners 0.5.49

  • Added missing feature type "integer" to classif.randomForest
  • Added missing feature type "logical" to {classif, regr}.randomForest

mlr3extralearners 0.5.48

  • Add rsm learner
  • fix list_mlr3learners() function. Now slower but correct.
  • Remove catboost from DESCRIPTION until it can be installed with pak
  • Fix typos in test templates
  • Update README

mlr3extralearners 0.5.47

  • Add mlr3proba dependencies into remotes (no longer on CRAN)
  • Correct documentation of gbm learner: default was incorrectly documented and the parameter was incorrectly referred to as keep_data instead of keep.data
  • Add catboost to the dependencies
  • Added LearnerSurvAorsf with key surv.aorsf. See https://github.com/bcjaeger/aorsf for more details on aorsf

mlr3extralearners 0.5.46

mlr3extralearners 0.5.45

  • Minor corrections in create_learner and the learner template.

mlr3extralearners 0.5.44

  • Corrected parameters in lightgbm learners
  • Implemented hotstarting for lightgbm learners
  • Adjusted lightgbm train and predict methods to changes in lightgbm dev version (https://github.com/mlr-org/mlr3extralearners/issues/217)
  • Added paramtests for lightgbm through webscraping

mlr3extralearners 0.5.43

  • Clean up test files
  • Fix installation of catboost in CI
  • Fix the create_learner function
  • Adjust templates for creation of learner
  • Split up “Parameter Changes” in sections “Custom mlr3 parameters” and “Custom mlr3 defaults”

mlr3extralearners 0.5.42

  • Fix bug in C50 learner: Weights were not passed correctly

  • Remove kerdiest Learner because it is not being maintained on CRAN anymore

mlr3extralearners 0.5.41

  • Fix bugs in learners lmer and J48

  • Remove predict type proba from J48

  • Delay loading of mlr3proba learners

mlr3extralearners 0.5.40

  • lightgbm:

    • Add parameter convert_categoricals
    • Validation split not respects grouping / stratification
    • Fixed bug

  • Docs: Renamed section “Custom mlr3 defaults” to “Parameter Changes”

  • Added labels to learners

mlr3extralearners 0.5.39

  • Remove extraTrees because it is no longer on CRAN and GH version has errors

  • Remove sketch_eps parameter from xgboost because it is no longer listed in the docs

mlr3extralearners 0.5.38

  • Added regr.lmer

mlr3extralearners 0.5.37

  • Improve docs and change doc layout
  • Fix typo in man-roxygen templates
  • Port mlr3proba learners (mlr3proba is no longer on CRAN)
  • Exclude relevant files in precommit

mlr3extralearners 0.5.36

  • Add missing ‘threads’ tag to respective parameters.

mlr3extralearners 0.5.35

  • Full installatio in workflow ‘test_selection’ (is faster than the previous approach, where selected packages were installed from CRAN)

mlr3extralearners 0.5.34

  • remove explicit mlr3misc:: (is imported)

mlr3extralearners 0.5.33

  • consistency: Use params in train and predict calls, even in learners that currently don’t have predict / train params. This allows easier correction of parameters by users.

mlr3extralearners 0.5.32

  • chore: add new parameters for kde and rfsrc

  • temporarily disable feat_all test for obliqeRSF (failed in $score() stage, because issue only happened in CI and could not be reproduced

mlr3extralearners 0.5.31

  • Many non-standard tags were included in the learners, these are removed
  • Some bugs in learners were fixed (survival rfsrc: “estimator” was incorrectly handled in .predict)
  • Minor refactorings in train methods of learners
  • Avoid partial argument matching: Some learners used “tag = …” instead of the correct “tags = …”

mlr3extralearners 0.5.30

  • Revert to using mlr3proba and survivalmodels CRAN version

mlr3extralearners 0.5.29

  • Change in vignette

mlr3extralearners 0.5.28

  • update randomForestSRC

mlr3extralearners 0.5.27

  • Update learner status page

mlr3extralearners 0.5.26

  • Fixed survivalmodel learners

mlr3extralearners 0.5.25

  • Introduce parameter early_stopping_split for lightgbm learners
  • Tidy description of R package
  • Udpate NEWS.md for previous releases

mlr3extralearners 0.5.24

  • Don’t allow integer for density estimator dens.plug

mlr3extralearners 0.5.23

  • Fix bug in lightgbm

mlr3extralearners 0.5.22

  • Style package using the mlr3 style

mlr3extralearners 0.5.21

  • Update files for creation of new learner
  • Fixes regarding create_learner
  • CI modifications

mlr3extralearners 0.5.20

  • Fix all parameter tests (run_paramtest was updated in mlr3 in November 2021)
  • paramtests were moved from inst/paramtest to tests/testthat
  • Change in the CI files: parameter tests and learner tests are now run together
  • formatting and other minor corrections

mlr3extralearners 0.5.19

  • Provide correct range for neighors argument for Cubist

mlr3extralearners 0.5.18

  • Allow integer as feature types for RWeka learners
  • Correction of RWeka tests

mlr3extralearners 0.5.17

  • Improve vignette

mlr3extralearners 0.5.16

  • Fix bug in AdaBoostM1 (control arg)

mlr3extralearners 0.5.15

  • Change in maintainer

mlr3extralearners 0.5.14

  • Fix bug regarding Weka control args.

mlr3extralearners 0.5.13

  • Fix categorical_features in {lightgbm} learners

mlr3extralearners 0.5.12

  • Patch for lightgbm updates

mlr3extralearners 0.5.11

  • Add option to not open files with create_learner

mlr3extralearners 0.5.10

  • Added params ignored_features and one_hot_max_size to classif.catboost

mlr3extralearners 0.5.9

  • Fixed bug that didn’t allow C parameter to be set for nu-regression

mlr3extralearners 0.5.8

  • Add regr.rvm and classif.lssvm

mlr3extralearners 0.5.7

mlr3extralearners 0.5.6

  • Fix learners requiring distr6. distr6 1.6.0 now forced and param6 added to suggests

mlr3extralearners 0.5.5

  • Bugfix regr.gausspr

mlr3extralearners 0.5.4

mlr3extralearners 0.5.3

  • Fixed bugs in catboost for classification
  • Removed factor feature types from catboost
  • Added install_catboost to make installation from catboost simpler

mlr3extralearners 0.5.2

  • Fixed learner tests

mlr3extralearners 0.5.1

  • Fixes bug in base parameter of {bart} learners

mlr3extralearners 0.5.0

  • Deprecated liblinear learners now removed
  • Internal changes to ParamSet representation
  • checkmate now imported

mlr3extralearners 0.4.9

  • Minor internal changes

mlr3extralearners 0.4.8

  • Added LearnerRegrCubist and LearnerRegrMars

mlr3extralearners 0.4.7

mlr3extralearners 0.4.6

  • Updates default cores for rfsrc learners to 1

mlr3extralearners 0.4.5

  • Fix RWeka tests (stochastic failures, implementation unaffected)

mlr3extralearners 0.4.3

  • Add support for custom families in all remaining mboost learners

mlr3extralearners 0.4.2

  • Fix broken partykit tests

mlr3extralearners 0.4.0

  • Added LearnerRegrGam and LearnerClassifGam with keys regr.gam and classif.gam from package mgcv.

mlr3extralearners 0.3.6

  • surv.coxboost now uses the GitHub version instead of CRAN (archived)

mlr3extralearners 0.3.4

  • Add support for custom families to regr.glmboost

mlr3extralearners 0.3.1

  • surv.svm now supports all feature types

mlr3extralearners 0.3.0

  • Added LearnerRegrLightGBM and LearnerClassifLightGBM with keys regr.lightgbm and classif.lightgbm respectively. Copied from mlr3learners.lightgbm
  • LearnerRegrLiblineaRX and LearnerClassifLiblineaRX deprecated in favour of only two learners (LearnerRegrLiblineaR and LearnerClassLiblineaR) with added hyper-parameters. Deprecated learners will be removed in v0.3.0.
  • Deprecated classif.nnet will be removed in v0.4.0.
  • Deprecated liblinearX will be removed in v0.4.0.

mlr3extralearners 0.2.0

  • dist = "logistic" has been removed from surv.parametric as it is unclear what this was previously predicting.
  • Added type = "tobit" for dist = "gaussian" so predictions can correspond with survival::survreg.
  • Added LearnerRegrGlm with the unique key regr.glm from package stats, which allows users to change the family hyperparameter when fitting generalized linear regression models.
  • Minor internal changes
  • Removed keeptrees parameter from classif.bart as this is forced internally
  • Fixed incorrect response and probability predictions in classif.bart
  • Added hyper-parameters to classif.earth and regr.earth
  • Added se predict type to regr.earth
  • Fixed predictions in regr.knn and classif.knn

mlr3extralearners 0.1.3

  • mlr3proba moved to Suggests
  • install_learners now additionally installs required mlr3 packages
  • Bugfix in surv.parametric occurring if feature names are switched between training and predicting
  • Deprecated classif.nnet, in the future please load from mlr3learners

mlr3extralearners 0.1.2

  • Fixes in crank and distr computation of all survival learners

mlr3extralearners 0.1.1

  • Patch for bugs in surv learners that were reversing the order of crank, see this issue for full details: https://github.com/mlr-org/mlr3proba/issues/165
  • response is no longer returned by surv.mboost, surv.blackboost, surv.glmboost, surv.gamboost or surv.parametric
  • Bugfix in surv.parametric with ph form
  • Bugfix in survivalmodelslearners which weren’t returning distr
  • surv.coxboost and surv.coxboost_cv can now only handle integer and numeric feature types, previous automated internal coercions were inconsistent with mlr3 design.

mlr3extralearners 0.1.0

  • Initial release. mlr3extralearners contains all learners from the mlr3learners organisation, which is now archived.