Description Usage Arguments Value Author(s) Examples
View source: R/partialDependence.R
Creates a list of partial dependence plots for each feature used by the model. Partial dependence is simply the average prediction path a model takes whilst iterating through unique values of a feature and keeping the rest of the features static
1 | partialDependence(train, trainedModel, sample = 0.1, seed = 1991)
|
train |
[data.frame | Required] Training set on which the model was trained |
trainedModel |
[mlr obj | Required] MLR trained moodel object |
sample |
[numeric | Optional] A number between 0 - 1 to sub-sample the training set for faster computational time. Default of 0.1 |
seed |
[integer | Optional] Random seed number for reproducable results. Default of 1991 |
List object containing a plot for each feature in the dataset.
Xander Horn
1 2 | mod <- mlr::train(makeLearner("classif.ranger"), iris.task)
partialDependence(train = iris, mod)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.