iml is an R package that interprets the behavior and explains predictions of machine learning models. It implements model-agnostic interpretability methods - meaning they can be used with any machine learning model.
Read more about the methods in the Interpretable Machine Learning book.
The package can be installed directly from CRAN and the development version from GitHub:
Changes of the packages can be accessed in the NEWS file.
First we train a Random Forest to predict the Boston median housing value. How does
lstat influence the prediction individually and on average? (Accumulated local effects)
library("iml") library("randomForest") data("Boston", package = "MASS") rf = randomForest(medv ~ ., data = Boston, ntree = 50) X = Boston[which(names(Boston) != "medv")] model = Predictor$new(rf, data = X, y = Boston$medv) effect = FeatureEffects$new(model) effect$plot(features = c("lstat", "age", "rm"))
Please check the contribution guidelines
If you use iml in a scientific publication, please cite it as:
© 2018 - 2020 Christoph Molnar
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