mlr_pipeops_learner_quantiles {mlr3pipelines}R Documentation

Wrap a Learner into a PipeOp to to predict multiple Quantiles

Description

Wraps a LearnerRegr into a PipeOp to predict multiple quantiles.

PipeOpLearnerQuantiles only supports LearnerRegrs that have quantiles as a possible pedict_type.

It produces quantile-based predictions for multiple quantiles in one PredictionRegr. This is especially helpful if the LearnerRegr can only predict one quantile (like for example LearnerRegrGBM in mlr3extralearners)

Inherits the ⁠$param_set⁠ (and therefore ⁠$param_set$values⁠) from the Learner it is constructed from.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpLearnerQuantiles$new(learner, id = NULL, param_vals = list())

Input and Output Channels

PipeOpLearnerQuantiles has one input channel named "input", taking a TaskRegr specific to the Learner type given to learner during construction; both during training and prediction.

PipeOpLearnerQuantiles has one output channel named "output", producing NULL during training and a PredictionRegr object during prediction.

The output during prediction is a PredictionRegr on the prediction input data that aggregates all results produced by the Learner for each quantile in quantiles. trained on the training input data.

State

The ⁠$state⁠ is set during training. It is a named list with the member:

Parameters

The parameters are exactly the parameters of the Learner wrapped by this object.

Internals

The ⁠$state⁠ is updated during training.

Fields

Fields inherited from PipeOp, as well as:

Methods

Methods inherited from PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Other Meta PipeOps: mlr_pipeops_learner, mlr_pipeops_learner_cv, mlr_pipeops_learner_pi_cvplus

Examples

library("mlr3")

task = tsk("boston_housing")
learner = lrn("regr.debug")
po = mlr_pipeops$get("learner_quantiles", learner)

po$train(list(task))
po$predict(list(task))

[Package mlr3pipelines version 0.7.2 Index]