featureImportance: Measures feature importance for any model provided

In XanderHorn/autoML: Automated machine learning

Description

Measures feature importance for any model provided

Usage

featureImportance(train, trainedModel, seed = 1991, topFeats = NULL,
  sample = 0.1, outputPath = NULL)

Arguments

train	[data.frame | Required] Training set on which the model was trained
trainedModel	[mlr obj | Required] MLR trained moodel object
seed	[integer | Optional] Random seed number for reproducable results. Default of 1991
sample	[numeric | Optional] A number between 0 - 1 to sub-sample the training set for faster computational time. Default of 0.1

Value

List object containing a data.frame and a plot object.

Author(s)

Xander Horn

Examples

mod <- mlr::train(makeLearner("classif.ranger"), iris.task)
featureImportance(train = iris, mod)

XanderHorn/autoML documentation built on Aug. 5, 2020, 11:45 a.m.