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 [email protected])

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 [email protected])
• 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()

1.1.0

See NEWS.md

v1.0.0

new release

0.9.0

See NEWS.md

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.

0.7.1

v0.7.1

release 0.7.1

Release 0.7.0

See NEWS.md

0.6.1

See NEWS.md for news.

Version 0.6.0

v0.6.0

version 0.6.0 (#256)

mlr3extralearners 0.5.49

https://github.com/mlr-org/mlr3extralearners/blob/main/NEWS.md