e2tree (version 0.2.0)

vimp: Variable Importance

Description

It calculate variable importance of an explainable tree

Usage

vimp(fit, data, type = "classification")

Value

a data frame containing variable importance metrics.

Arguments

fit

is a e2tree object

data

is a data frame in which to interpret the variables named in the formula.

type

Specify the type. The default is ‘classification’, otherwise ‘regression’.

Examples

# \donttest{
## 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, ]
validation <- iris[-train_ind, ]
response_training <- training[,5]
response_validation <- validation[,5]

# 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)

vimp(tree, training, type = "classification")


## 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, ]
validation <- mtcars[-train_ind, ]
response_training <- training[,1]
response_validation <- validation[,1]

# 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)

vimp(tree, training, type = "regression")

# }