mlr_pipeops_learner_pi_cvplus {mlr3pipelines}R Documentation

Wrap a Learner into a PipeOp with Cross-validation Plus Confidence Intervals as Predictions

Description

Wraps an mlr3::Learner into a PipeOp.

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

Using PipeOpLearnerPICVPlus, it is possible to embed a mlr3::Learner into a Graph. PipeOpLearnerPICVPlus can then be used to perform cross validation plus (or jackknife plus). During training, PipeOpLearnerPICVPlus performs cross validation on the training data. During prediction, the models from the training stage are used to construct predictive confidence intervals for the prediction data based on out-of-fold residuals and out-of-fold predictions.

Format

R6Class object inheriting from PipeOp.

Construction

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

Input and Output Channels

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

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

The output during prediction is a PredictionRegr with predict_type quantiles on the prediction input data. The alpha and 1 - alpha quantiles are the quantiles of the prediction interval produced by the cross validation plus method. The response is the median of the prediction of all cross validation models on the prediction data.

State

The ⁠$state⁠ is a named list with members:

This state is given the class "pipeop_learner_cv_state".

Parameters

The parameters of the Learner wrapped by this object, as well as:

Internals

The ⁠$state⁠ is updated during training.

Fields

Fields inherited from PipeOp, as well as:

Methods

Methods inherited from PipeOp.

References

Barber RF, Candes EJ, Ramdasa A, Tibshirani RJ (2021). “Predictive inference with the jackknife+.” Annals of Statistics, 49, 486–507. doi:10.1214/20-AOS1965.

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_quantiles, 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_quantiles

Examples


library("mlr3")

task = tsk("mtcars")
learner = lrn("regr.rpart")
lrncvplus_po = mlr_pipeops$get("learner_pi_cvplus", learner)

lrncvplus_po$train(list(task))
lrncvplus_po$predict(list(task))


[Package mlr3pipelines version 0.8.0 Index]