mlr_graphs_robustify

From mlr3pipelines v0.3.0 by Martin Binder

Robustify a learner

Creates a Graph that can be used to robustify any subsequent learner. Performs the following steps:

Drops empty factor levels using PipeOpFixFactors
Imputes numeric features using PipeOpImputeHist and PipeOpMissInd
Imputes factor features using PipeOpImputeOOR
Encodes factors using one-hot-encoding. Factors with a cardinality > max_cardinality are collapsed using [PipeOpCollapseFactors`].
If scaling, numeric features are scaled to mean 0 and standard deviation 1.

The graph is built conservatively, i.e. the function always tries to assure everything works. If a learner is provided, some steps can be left out, i.e. if the learner can deal with factor variables, no encoding is performed.

Usage

pipeline_robustify(
  task = NULL,
  learner = NULL,
  impute_missings = NULL,
  factors_to_numeric = NULL,
  max_cardinality = 1000
)

Arguments

task: Task A Task to create a robustifying pipeline for. Optional, if omitted, the full pipeline is created.
learner: Learner A learner to create a robustifying pipeline for. Optional, if omitted, a more conservative pipeline is built.
impute_missings: logical(1) | NULL Should missing values be imputed? Defaults to NULL, i.e imputes if the task has missing values and the learner can not handle them.
factors_to_numeric: logical(1) | NULL Should factors be encoded? Defaults to NULL, i.e encodes if the task has factors and the learner can not handle factors.
max_cardinality: integer(1) Maximum number of factor levels allowed. See above. Default: 1000.

Value

Graph

Examples

# NOT RUN {
library(mlr3)
lrn = lrn("regr.rpart")
task = mlr_tasks$get("boston_housing")
gr = pipeline_robustify(task, lrn) %>>% po("learner", lrn)
resample(task, GraphLearner$new(gr), rsmp("holdout"))
# }

Documentation reproduced from package mlr3pipelines, version 0.3.0, License: LGPL-3

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