mlr_pipeops_learner_pi_cvplus: Wrap a Learner into a PipeOp with Cross-validation Plus...

In mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

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())

• learner :: LearnerRegr LearnerRegr to use for the cross validation models in the Cross Validation Plus method. This argument is always cloned; to access the Learner inside PipeOpLearnerPICVPlus by-reference, use ⁠$learner⁠.
  

• id :: character(1) Identifier of the resulting object, internally defaulting to the id of the Learner being wrapped.

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default is 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:

• cv_model_states :: list
  List of the state of each cross validation model created by the Learner's ⁠$.train()⁠ function during resampling with method "cv".

• residuals :: data.table
data.table with columns fold and residual. Lists the Regression residuals for each observation and cross validation fold.

This state is given the class "pipeop_learner_cv_state".

Parameters

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

• folds :: numeric(1)
  Number of cross validation folds. Initialized to 3.

• alpha :: numeric(1)
  Quantile to use for the cross validation plus prediction intervals. Initialized to 0.05.

Internals

The ⁠$state⁠ is updated during training.

Fields

Fields inherited from PipeOp, as well as:

• learner :: Learner
Learner that is being wrapped. Read-only.

• learner_model :: Learner or list
  If the PipeOpLearnerPICVPlus has been trained, this is a list containing the Learners of the cross validation models. Otherwise, this contains the Learner that is being wrapped. Read-only.

• predict_type
  Predict type of the PipeOpLearnerPICVPlus, which is always ⁠"response" "quantiles"⁠. This can be different to the predict type of the Learner that is being wrapped.

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. \Sexpr[results=rd]{tools:::Rd_expr_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))

mlr3pipelines documentation built on Aug. 8, 2025, 7:19 p.m.