Dot plot of covariates effects

Plots the effects (partial dependence or accumulated local effects) of the covariates of a supervised learning model in a single a dot plot.

ggForestEffects(dt, vline=0, xlabel="", ylabel="", main="")

Arguments

dt

data frame. Must have three columns : one with the names of the covariates (named "var"), one with the names of the categories of the covariates (named "cat"), one with the values of the effects (named "value"). Typically the result of GetAleData or GetPartialData functions.

vline

numeric. Coordinate on the x axis where a vertical line is added.

xlabel

character. Title of the x axis.

ylabel

character. Title of the y axis.

main

character. Title of the plot.

Note

There should be no duplicated categories. If it is the case, duplicated categories have to be renamed in dt prior to running ggForestEffects.

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 = cforest_unbiased(mtry=2))
  ale <- GetAleData(iris.cf)
  ale$cat <- paste(ale$var,ale$cat,sep='_')  # to avoid duplicated categories
  ggForestEffects(ale)
}