xpy: Explainability and matchplot for PDP

In g-rho/xPDPy: Numerical and visual tools to analyse the explainability of partial dependence functions

Description

Computes explainability and matchplots for a partial dependence function.

Usage

xpy(
  model,
  x,
  vnames,
  viz = TRUE,
  parallel = TRUE,
  sample.frac = 1,
  pfunction = NULL,
  ...
)

Arguments

model	A model with corresponding predict function that returns numeric values.
x	Data frame.
vnames	Character vector of the variable set for which the patial dependence function is to be computed.
viz	Logical specifying whether a matchplot should be created.
parallel	Logical specifying whether computation should be parallel.
sample.frac	fraction-size for sampling of x.
pfunction	User generated predict function with arguments pfunction(model, newdata).
...	Further arguments to be passed to the predict() method of the model.

Value

Numeric value with the explainability of the partial dependence function for the variables specified in vnames.

Author(s)

[e-mail]

References

• Szepannek, G. (2019): How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models, arXiv:1910.13376 [stat.ML].

Examples

library(pdp)
library(randomForest)
data(boston)
set.seed(42)
boston.rf <- randomForest(cmedv ~ ., data = boston)
xpy(boston.rf, boston, c("lstat", "rm"))

# example 2: user-defined predict function for classification
library(klaR)
data("GermanCredit")

library(randomForest)
rfGC <- randomForest(credit_risk ~., data = GermanCredit)

# define predict function that returns continuous value (here: prob(yes))
pfunc = function(model, x) predict(model, x, type = "prob")[,2]
xpy(rfGC, GermanCredit, vnames = "duration", parallel = FALSE, pfunction = pfunc)

g-rho/xPDPy documentation built on Dec. 20, 2021, 9:43 a.m.