vignettes/learners/additional-learners.Rmd
additional-learners.RmdThe following learners are shipped in their own packages to keep the {mlr3learners} package small. Not all of them have necessarily been developed by the mlr-org team. Some can possibly be maintained by external persons.
See the section in the {mlr3learners} README on how to add/request a new learner.
Resources for adding a new learner (summary)
Besides installing from GitHub via remotes::install_github() it is possible to have a CRAN-like installation via install.packages() by installing the learners from mlr3learners.drat (instructions in the README).
| CI Status | Metadata | mlr3 Learner ID |
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mlr3learners.c50 Upstream function: C50::C5.0() Maintainer: @henrifnk |
"classif.C5.0' |
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mlr3learners.coxboost Upstream function: coxboost::coxboost() coxboost::cv.coxboost Maintainer: @RaphaelS1 |
"surv.coxboost" "surv.cvcoxboost"
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mlr3learners.extratrees Upstream function: extraTrees::extraTrees() Maintainer: @be-marc |
"classif.extraTrees" "regr.extraTrees"
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mlr3learners.flexsurv Upstream function: flexsurv::flexsurvspline() Maintainer: @RaphaelS1 |
"surv.flexible" |
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mlr3learners.fnn Upstream function: FNN::knn() Maintainer: @be-marc |
"classif.fnn" "regr.fnn"
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mlr3learners.gbm Upstream function: gbm::gbm() Maintainer: @be-marc |
"classif.gbm" "regr.gbm" "surv.gbm"
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mlr3learners.gss Upstream function: gss::ssden() Maintainer: @RaphaelS1 |
"dens.spline" |
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mlr3learners.kerdiest Upstream function: kerdiest::kde() Maintainer: @RaphaelS1 |
"dens.kdeKD" |
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mlr3learners.kernlab Upstream function: kernlab::ksvm() Maintainer: @be-marc |
"classif.ksvm" "regr.ksvm"
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mlr3learners.ks Upstream function: ks::kde() Maintainer: @RaphaelS1 |
"dens.kdeKS" |
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mlr3learners.locfit Upstream function: locfit::density.lf() Maintainer: @RaphaelS1 |
"dens.locfit" |
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mlr3learners.liblinear Upstream function: LiblineaR::LiblineaR() Maintainer: @be-marc |
"classif.liblinearl1l2svc" "classif.liblinearl1logreg" "classif.liblinearl2l1svc" "classif.liblinearl2l2svc" "classif.liblinearl2logreg" "classif.liblinearmulticlasssvc" "regr.liblinearl2l1svr" "regr.liblinearl2l2svr"
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mlr3learners.logspline Upstream function: logspline::logspline() Maintainer: @RaphaelS1 |
"surv.logspline" |
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mlr3learners.mboost Upstream function: mboost::gamboost() mboost::glmboost() Maintainer: @be-marc |
"classif.gamboost" "classif.glmboost" "regr.gamboost" "regr.glmboost" "surv.blackboost" "surv.gamboost" "surv.glmboost" "surv.mboost"
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mlr3learners.np Upstream function: np::npudens() Maintainer: @RaphaelS1 |
"dens.mixed" |
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mlr3learners.obliquersf Upstream function: obliqueRSF::ORSF() Maintainer: @RaphaelS1 |
"surv.obliqueRSF" |
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mlr3learners.partytkit Upstream function: partykit::ctree() partykit::cforest() partykit::mob() Maintainer: @sumny |
"classif.ctree" "regr.ctree" "classif.cforest" "regr.cforest" "classif.mob" "regr.mob"
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mlr3learners.penalized Upstream function: penalized::penalized() Maintainer: @RaphaelS1 |
"surv.penalized" |
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mlr3learners.pendensity Upstream function: pendensity::pendensity() Maintainer: @RaphaelS1 |
"dens.pen" |
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mlr3learners.plugdensity Upstream function: plugdensity::plugin.density() Maintainer: @RaphaelS1 |
"dens.plug" |
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mlr3learners.proba Maintainer: @RaphaelS1 |
"surv.akritas" "surv.dnn"
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mlr3learners.pycox Upstream function: pycox.models.CoxTime pycox.models.DeepHitSingle pycox.models.CoxPH pycox.models.LogisticHazard pycox.models.PCHazard Maintainer: @RaphaelS1 |
"surv.coxtime" "surv.deephit" "surv.deepsurv" "surv.loghaz" "surv.pchazard"
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mlr3learners.randomforest Upstream function: randomForest::randomForest() Maintainer: @pat-s |
"classif.randomForest" "regr.randomForest"
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mlr3learners.randomforestsrc Upstream function: randomForestSRC::rfsrc() Maintainer: @RaphaelS1 |
"classif.rfsrc" "regr.rfsrc" "surv.rfsrc"
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mlr3learners.rweka Upstream function: RWeka::AdaBoostM1() RWeka::IBk() RWeka::J48() RWeka::JRip() RWeka::LMT() RWeka::M5Rules() RWeka::OneR() RWeka::PART() Maintainer: @henrifnk |
"classif.AdaBoostM1" "classif.IBk" "classif.J48" "classif.JRip" "classif.LMT" "classif.OneR" "classif.PART" "regr.M5Rules" "regr.IBk"
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mlr3learners.sm Upstream function: sm::sm.density() Maintainer: @RaphaelS1 |
"dens.nonpar" |
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mlr3learners.survival Upstream function: survival::survfit() survival::survreg() Maintainer: @RaphaelS1 |
"surv.nelson" "surv.parametric"
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mlr3learners.survivalsvm Upstream function: survivalsvm::survivalsvm() Maintainer: @RaphaelS1 |
"surv.svm" |
| CI Status | Metadata | mlr3 Learner ID |
|---|---|---|
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mlr3learners.countglm Upstream function: stats::glm() Maintainer: @pkopper |
"classif.countglm""regr.countglm"
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mlr3learners.dbarts Upstream function: dbarts::bart() Maintainer: @ck37 |
"classif.dbarts" "regr.dbarts"
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mlr3learners.h2o Upstream function: h2o:glm() h2o:glmr() h2o:gbm() h2o:randomForest() h2o:deeplearning() Maintainer: @be-marc |
"classif.h2o.glm" "classif.h2o.glmr" "classif.h2o.gbm" "classif.h2o.randomForest" "classif.h2o.deeplearning" "regr.h2o.glm""regr.h2o.glmr" "regr.h2o.gbm" "regr.h2o.randomForest" "regr.h2o.deeplearning"
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mlr3learners.lightgbm Upstream function: lightgbm::lgb.train() Maintainer: @kapsner |
"classif.lgbm" "regr.lgbm"
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mlr3learners.nnet Upstream function: nnet::nnet Maintainer: @henrifnk |
"classif.nnet" |
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mlr3learners.rborist Upstream function: Rborist::Rborist() Maintainer: @pkopper |
"classif.Rborist" "regr.Rborist"
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mlr3learners.catboost Upstream function: catboost::catboost.train() catboost::catboost.predict() Maintainer: @sumny |
"classif.catboost" "regr.catboost"
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