```
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$new(mod, loss = "mae")
# Plot the results directly
plot(imp)
# Since the result is a ggplot object, you can extend it:
library("ggplot2")
plot(imp) + theme_bw()
# If you want to do your own thing, just extract the data:
imp.dat <- imp$results
head(imp.dat)
ggplot(imp.dat, aes(x = feature, y = importance)) +
geom_point() +
theme_bw()
# We can also look at the difference in model error instead of the ratio
imp <- FeatureImp$new(mod, loss = "mae", compare = "difference")
# Plot the results directly
plot(imp)
# We can calculate feature importance for a subset of features
imp <- FeatureImp$new(mod, loss = "mae", features = c("crim", "zn", "indus"))
plot(imp)
# We can calculate joint importance of groups of features
groups = list(
grp1 = c("crim", "zn", "indus", "chas"),
grp2 = c("nox", "rm", "age", "dis"),
grp3 = c("rad", "tax", "ptratio", "black", "lstat")
)
imp <- FeatureImp$new(mod, loss = "mae", features = groups)
plot(imp)
# FeatureImp also works with multiclass classification.
# In this case, the importance measurement regards all classes
tree <- rpart(Species ~ ., data = iris)
X <- iris[-which(names(iris) == "Species")]
y <- iris$Species
mod <- Predictor$new(tree, data = X, y = y, type = "prob")
# For some models we have to specify additional arguments for the predict function
imp <- FeatureImp$new(mod, loss = "ce")
plot(imp)
# For multiclass classification models, you can choose to only compute
# performance for one class.
# Make sure to adapt y
mod <- Predictor$new(tree,
data = X, y = y == "virginica",
type = "prob", class = "virginica"
)
imp <- FeatureImp$new(mod, loss = "ce")
plot(imp)
```

Run the code above in your browser using DataCamp Workspace