createDisMatrix {e2tree} | R Documentation |
Dissimilarity matrix
Description
The function createDisMatrix creates a dissimilarity matrix among observations from an ensemble tree.
Usage
createDisMatrix(
ensemble,
data,
label,
parallel = list(active = FALSE, no_cores = 1),
verbose = FALSE
)
Arguments
ensemble |
is an ensemble tree object (for the moment ensemble works only with random forest objects) |
data |
is a data frame containing the variables in the model. It is the data frame used for ensemble learning. |
label |
is a character. It indicates the response label. |
parallel |
A list with two elements: |
verbose |
Logical. If TRUE, the function prints progress messages and other information during execution. If FALSE (the default), messages are suppressed. |
Value
A dissimilarity matrix. This is a dissimilarity matrix measuring the discordance between two observations concerning a given classifier of a random forest model.
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))
## 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))