mlr_pipeops_regravg: Weighted Prediction Averaging

In mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

Weighted Prediction Averaging

Description

Perform (weighted) prediction averaging from regression Predictions by connecting PipeOpRegrAvg to multiple PipeOpLearner outputs.

The resulting "response" prediction is a weighted average of the incoming "response" predictions. Aggregation of "se" predictions is controlled by the se_aggr parameter (see below). When "se" is not requested or se_aggr = "none", "se" is dropped.

Format

R6Class inheriting from PipeOpEnsemble/PipeOp.

"se" Aggregation

Let there be K incoming predictions with weights w (sum to 1). For a given row j, denote per-model means mu_i[j] and, if available, per-model standard errors se_i[j]. Define

mu_bar[j]      = sum_i w[i] * mu_i[j]
var_between[j] = sum_i w[i] * (mu_i[j] - mu_bar[j])^2 # weighted var of means
var_within[j]  = sum_i w[i] * se_i[j]^2               # weighted mean of SE^2s

The following aggregation methods are available:

• se_aggr = "predictive" – Within + Between (mixture/predictive SD)

  se[j] = sqrt(var_within[j] + var_between[j])
  

  Interpretation. Treats each incoming se_i as that model's predictive SD at the point (or, if the learner reports SE of the conditional mean–as many mlr3 regression learners do–then as that mean-SE). The returned se is the SD of the mixture ensemble under weighted averaging: it increases when base models disagree (epistemic spread) and when individual models are uncertain (aleatoric spread). Notes. If se_i represents mean SE (common in predict.lm(se.fit=TRUE)-style learners), the result aggregates those mean-SEs and still adds model disagreement correctly, but it will underestimate a true predictive SD that would additionally include irreducible noise. Requires "se" to be present from all inputs.

• se_aggr = "mean" – SE of the weighted average of means under equicorrelation With a correlation parameter se_aggr_rho = rho, assume Cov(mu_i_hat, mu_j_hat) = rho * se_i * se_j for all i != j. Then

  # components:
  a[j] = sum_i (w[i]^2 * se_i[j]^2)
  b[j] = (sum_i w[i] * se_i[j])^2
  var_mean[j] = (1 - rho) * a[j] + rho * b[j]
  se[j] = sqrt(var_mean[j])
  

  Interpretation. Returns the standard error of the averaged estimator ⁠sum_i w[i] * mu_i⁠, not a predictive SD. Use when you specifically care about uncertainty of the averaged mean itself. Notes. rho is clamped to the PSD range ⁠[-1/(K-1), 1]⁠ for K > 1. Typical settings: rho = 0 (assume independence; often optimistic for CV/bagging) and rho = 1 (perfect correlation; conservative and equal to the weighted arithmetic mean of SEs). Requires "se" from all inputs.

• se_aggr = "within" – Within-model component only

  se[j] = sqrt(var_within[j])
  

  Interpretation. Aggregates only the average per-model uncertainty and ignores disagreement between models. Useful as a diagnostic of the aleatoric component; not a full ensemble uncertainty. Notes. Typically underestimates the uncertainty of the ensemble prediction when models disagree. Requires "se" from all inputs.

• se_aggr = "between" – Between-model component only (works without "se")

  se[j] = sqrt(var_between[j])
  

  Interpretation. Captures only the spread of the base means (epistemic/model disagreement). Notes. This is the only method that does not use incoming "se". It is a lower bound on a full predictive SD, because it omits within-model noise.

• se_aggr = "none" – Do not return "se" "se" is dropped from the output prediction.

Relationships and edge cases. For any row, se("predictive") >= max(se("within"), se("between")). With a single input (K = 1), "predictive" and "within" return the input "se", "between" returns 0. Methods "predictive", "mean", and "within" require all inputs to provide "se"; otherwise aggregation errors.

Weights can be set as a parameter; if none are provided, defaults to equal weights for each prediction.

Construction

PipeOpRegrAvg$new(innum = 0, collect_multiplicity = FALSE, id = "regravg", param_vals = list())

• innum :: numeric(1)
  Determines the number of input channels. If innum is 0 (default), a vararg input channel is created that can take an arbitrary number of inputs.

• collect_multiplicity :: logical(1)
  If TRUE, the input is a Multiplicity collecting channel. This means, a Multiplicity input, instead of multiple normal inputs, is accepted and the members are aggregated. This requires innum to be 0. Default is FALSE.

• id :: character(1) Identifier of the resulting object, default "regravg".

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

Input and Output Channels

Input and output channels are inherited from PipeOpEnsemble. Instead of a Prediction, a PredictionRegr is used as input and output during prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

The parameters are the parameters inherited from the PipeOpEnsemble, as well as:

• se_aggr :: character(1)
  Controls how incoming "se" values are aggregated into an ensemble "se". One of "predictive", "mean", "within", "between", "none". See the description above for definitions and interpretation.

• se_aggr_rho :: numeric(1)
  Equicorrelation parameter used only for se_aggr = "mean". Interpreted as the common correlation between per-model mean estimators. Recommended range ⁠[0, 1]⁠; values are clamped to ⁠[-1/(K-1), 1]⁠ for validity.

Internals

Inherits from PipeOpEnsemble by implementing the private$weighted_avg_predictions() method.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpEnsemble/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_info, mlr_pipeops_isomap, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, 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_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 Multiplicity PipeOps: Multiplicity(), PipeOpEnsemble, mlr_pipeops_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_replicate

Other Ensembles: PipeOpEnsemble, mlr_learners_avg, mlr_pipeops_classifavg, mlr_pipeops_ovrunite

Examples

library("mlr3")

# Simple Bagging for Regression
gr = ppl("greplicate",
  po("subsample") %>>%
  po("learner", lrn("regr.rpart")),
  n = 5
) %>>%
  po("regravg")

resample(tsk("mtcars"), GraphLearner$new(gr), rsmp("holdout"))

mlr3pipelines documentation built on Nov. 7, 2025, 9:06 a.m.