vimp: Variable Importance

In e2tree: Explainable Ensemble Trees

Variable Importance

Description

Computes variable importance for an E2Tree model based on mean impurity decrease and (for classification) mean accuracy decrease.

Usage

vimp(fit, data, type = NULL)

Arguments

fit	An e2tree object.
data	A data frame containing the variables in the model.
type	Character string: "classification" or "regression". If NULL (default), the type is automatically detected from the e2tree object.

Value

A list containing:

vimp

A data frame with variable importance metrics.

g_imp

A ggplot bar chart of Mean Impurity Decrease.

g_acc

(Classification only) A ggplot bar chart of Mean Accuracy Decrease.

Examples

## Classification:
data(iris)

# Create training and validation set:
smp_size <- floor(0.75 * nrow(iris))
train_ind <- sample(seq_len(nrow(iris)), size = smp_size)
training <- iris[train_ind, ]

# Perform training:
ensemble <- randomForest::randomForest(Species ~ ., data=training,
importance=TRUE, proximity=TRUE)

D <- createDisMatrix(ensemble, data=training, label = "Species",
                             parallel = list(active=FALSE, no_cores = 1))

setting=list(impTotal=0.1, maxDec=0.01, n=2, level=5)
tree <- e2tree(Species ~ ., training, D, ensemble, setting)

vi <- vimp(tree, training)
vi$vimp
vi$g_imp


## Regression
data("mtcars")

# Create training and validation set:
smp_size <- floor(0.75 * nrow(mtcars))
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)
training <- mtcars[train_ind, ]

# Perform training
ensemble = randomForest::randomForest(mpg ~ ., data=training, ntree=1000,
importance=TRUE, proximity=TRUE)

D = createDisMatrix(ensemble, data=training, label = "mpg",
                         parallel = list(active=FALSE, no_cores = 1))

setting=list(impTotal=0.1, maxDec=(1*10^-6), n=2, level=5)
tree <- e2tree(mpg ~ ., training, D, ensemble, setting)

vi <- vimp(tree, training)
vi$vimp
vi$g_imp

e2tree documentation built on May 15, 2026, 5:06 p.m.