Description Usage Arguments Value Author(s) Examples
View source: R/autoInterpret.R
Generates various plots for model interpretability
1 2 | autoInterpret(train, trainedModel, sample = NULL, seed = 1991,
verbose = TRUE)
|
train |
[data.frame | Required] Training set on which the original model was trained |
trainedModel |
[mlr model object | Required] A trained model using the mlr pacakge or produced via autoLearn |
sample |
[numeric | Optional] A number between 0 - 1 to sub-sample the training set for faster computational time. Default of NULL which will result in a small sample |
seed |
[integer | Optional] Random seed number for reproducable results. Default of 1991 |
List containing plots
Xander Horn
1 2 3 4 5 | train <- iris
mod <- mlr::train(makeLearner("classif.ranger", predict.type = "prob"), iris.task)
plots <- autoInterpret(train = iris,
trainedModel = mod)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.