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I am trying to get the FeatureImp function from the IML package to work, but it keeps throwing an error. Below is an example from the diamonds dataset, on which I train a random forest model.

library(iml)
library(caret)
library(randomForest)
data(diamonds)
# create some binary classification target (without specific meaning)
diamonds$target <- as.factor(ifelse(diamonds$color %in% c("D", "E", "F"), "X", "Y"))
# drop categorical variables (to keep it simple for demonstration purposes)
diamonds <- subset(diamonds, select = -c(color, clarity, cut))
# train model
mdl_diamonds <- train(target ~ ., method = "rf", data = diamonds)
# create iml predictor
x_pred <- Predictor$new(model = mdl_diamonds, data = diamonds[, 1:7], y = diamonds$target, type = "prob")
# calculate feature importance
x_imp <- FeatureImp$new(x_pred, loss = "mae")

This ends with the following error:

Error in if (self$original.error == 0) { :
  missing value where TRUE/FALSE needed
In addition: Warning message:
In Ops.factor(actual, predicted) : ‘-’ not meaningful for factors

I don't understand what I'm doing wrong. Can anyone give me clue?

I'm working on R version 3.5.1, iml package version 0.9.0.

pat-s
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Eric Becker
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2 Answers2

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I have found the problem. I was using "mae" as the loss function, which is - I could have known - not applicable for a classification target. Using "ce" or "f1" returns output as expected.

Eric Becker
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because it's random forest. so try loss = 'ce'.

張靜中
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