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Create A Graph to Perform Stacking.

Source: R/pipeline_stacking.R
mlr_graphs_stacking.Rd

Create a new Graph for stacking. A stacked learner uses predictions of several base learners and fits a super learner using these predictions as features in order to predict the outcome.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_stacking(
  base_learners,
  super_learner,
  method = "cv",
  folds = 3,
  use_features = TRUE
)

Arguments

base_learners

list of Learner
A list of base learners.

super_learner

Learner
The super learner that makes the final prediction based on the base learners.

method

character(1)
"cv" (default) for building a super learner using cross-validated predictions of the base learners or "insample" for building a super learner using the predictions of the base learners trained on all training data.

folds

integer(1)
Number of cross-validation folds. Only used for method = "cv". Default 3.

use_features

logical(1)
Whether the original features should also be passed to the super learner. Default TRUE.

Value

Graph

Examples

library(mlr3)
library(mlr3learners)

base_learners = list(
  lrn("classif.rpart", predict_type = "prob"),
  lrn("classif.nnet", predict_type = "prob")
)
super_learner = lrn("classif.log_reg")

graph_stack = pipeline_stacking(base_learners, super_learner)
graph_learner = as_learner(graph_stack)
graph_learner$train(tsk("german_credit"))
#> # weights:  172
#> initial  value 846.477289 
#> final  value 610.864302 
#> converged
#> # weights:  172
#> initial  value 417.567736 
#> final  value 407.847240 
#> converged
#> # weights:  172
#> initial  value 466.311391 
#> final  value 408.206169 
#> converged
#> # weights:  172
#> initial  value 498.245682 
#> final  value 405.651390 
#> converged