R: Estimate Regression distr Predict Type Pipeline

mlr_graphs_probregrcompositor {mlr3proba} | R Documentation

## Estimate Regression distr Predict Type Pipeline

### Description

Wrapper around PipeOpProbregrCompositor to simplify Graph creation.

### Usage

    pipeline_probregrcompositor(
      learner,
      learner_se = NULL,
      dist = "Normal",
      graph_learner = FALSE
    )

### Arguments

`learner` | `[mlr3::Learner]|[mlr3pipelines::PipeOp]|[mlr3pipelines::Graph]` Either a `Learner` which will be wrapped in mlr3pipelines::PipeOpLearner, a `PipeOp` which will be wrapped in mlr3pipelines::Graph or a `Graph` itself. Underlying `Learner` should be LearnerRegr.
`learner_se` | `[mlr3::Learner]|[mlr3pipelines::PipeOp]` Optional LearnerRegr with predict_type `se` to estimate the standard error. If left `NULL` then `learner` must have `se` in predict_types.
`dist` | `character(1)` Location-scale distribution to use for composition. Current possibilities are' `"Cauchy", "Gumbel", "Laplace", "Logistic", "Normal` (default).
`graph_learner` | `logical(1)` If `TRUE` returns wraps the Graph as a GraphLearner otherwise (default) returns as a `Graph`.

### Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

### See Also

Other pipelines: `mlr_graphs_crankcompositor`, `mlr_graphs_distrcompositor`, `mlr_graphs_survaverager`, `mlr_graphs_survbagging`, `mlr_graphs_survtoregr`

### Examples

    ## Not run:
    if (requireNamespace("mlr3pipelines", quietly = TRUE) &&
      requireNamespace("rpart", quietly = TRUE)) {
    library("mlr3")
    library("mlr3pipelines")

    task = tsk("boston_housing")

    # method 1 - one learner for response and se
    pipe = ppl(
      "probregrcompositor",
      learner = lrn("regr.featureless", predict_type = "se"),
      dist = "Normal"
    )
    pipe$train(task)
    pipe$predict(task)

    # method 2 - one learner for response and one for se
    pipe = ppl(
      "probregrcompositor",
      learner = lrn("regr.rpart"),
      learner_se = lrn("regr.featureless", predict_type = "se"),
      dist = "Logistic"
    )
    pipe$train(task)
    pipe$predict(task)
    }

    ## End(Not run)

[Package mlr3proba version 0.3.2 Index]