plot.FeatureImp: Plot Feature Importance

In iml: Interpretable Machine Learning

Plot Feature Importance

Description

plot.FeatureImp() plots the feature importance results of a FeatureImp object.

Usage

## S3 method for class 'FeatureImp'
plot(x, sort = TRUE, ...)

Arguments

x	A FeatureImp object
sort	logical. Should the features be sorted in descending order? Defaults to TRUE.
...	Further arguments for the objects plot function

Details

The plot shows the importance per feature.

When n.repetitions in FeatureImp$new was larger than 1, then we get multiple importance estimates per feature. The importance are aggregated and the plot shows the median importance per feature (as dots) and also the 90%-quantile, which helps to understand how much variance the computation has per feature.

Value

ggplot2 plot object

See Also

FeatureImp

Examples

library("rpart")
# We train a tree on the Boston dataset:
data("Boston", package = "MASS")
tree <- rpart(medv ~ ., data = Boston)
y <- Boston$medv
X <- Boston[-which(names(Boston) == "medv")]
mod <- Predictor$new(tree, data = X, y = y)

# Compute feature importances as the performance drop in mean absolute error
imp <- FeatureImp$new(mod, loss = "mae")

# Plot the results directly
plot(imp)

iml documentation built on May 29, 2024, 1:59 a.m.