Accumulated Local Effects for a conditional random forest.

Computes the Accumulated Local Effects for several covariates in a conditional random forest and gathers them into a single data frame.

GetAleData(object, xnames=NULL, order=1, grid.size=20, parallel=FALSE)

Arguments

object: An object as returned by cforest (or fastcforest).
xnames: A character vector of the covariates for which to compute the Accumulated Local Effects. If NULL (default), ALE are computed for all the covariates in the model. Should be of length 2 for 2nd order ALE.
order: An integer indicating whether to compute 1st order ALE (1, default) or 2nd order ALE (2).
grid.size: The size of the grid for evaluating the predictions. Default is 20.
parallel: Logical indicating whether or not to run the function in parallel using a backend provided by the foreach package. Default is FALSE.

Details

The computation of Accumulated Local Effects uses FeatureEffect function from iml package for each covariate. The results are then gathered and reshaped into a friendly data frame format.

Value

A data frame with covariates, their categories and their accumulated local effects.

References

Apley, D. W., Zhu J. "Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models". arXiv:1612.08468v2, 2019.

Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019. https://christophm.github.io/interpretable-ml-book/.

Author

Nicolas Robette

Examples

  if (FALSE) {
  data(iris)
  iris2 = iris
  iris2$Species = factor(iris$Species == "versicolor")
  iris.cf = party::cforest(Species ~ ., data = iris2, 
              controls = party::cforest_unbiased(mtry=2, ntree=50))
  GetAleData(iris.cf)
}