vimp {e2tree} | R Documentation |
Variable Importance
Description
It calculate variable importance of an explainable tree
Usage
vimp(fit, data, type = "classification")
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’. |
Value
a data frame containing variable importance metrics.
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, ]
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")
[Package e2tree version 0.2.0 Index]