ddalpha (version 1.3.16)

ddalpha.getErrorRateCV: Test DD-Classifier

Description

Performs a cross-validation procedure over the given data. On each step every numchunks observation is removed from the data, the DD-classifier is trained on these data and tested on the removed observations.

Usage

ddalpha.getErrorRateCV (data, numchunks = 10,  ...)

Value

errors

the part of incorrectly classified data

time

the mean training time

time_sd

the standard deviation of training time

Arguments

data

Matrix containing training sample where each of \(n\) rows is one object of the training sample where first \(d\) entries are inputs and the last entry is output (class label).

numchunks

number of subsets of testing data. Equals to the number of times the classifier is trained.

...

additional parameters passed to ddalpha.train

See Also

ddalpha.train to train the DD\(\alpha\)-classifier, ddalpha.classify for classification using DD\(\alpha\)-classifier, ddalpha.test to test the DD-classifier on particular learning and testing data, ddalpha.getErrorRatePart to perform a benchmark study of the DD-classifier on particular data.

Examples

# Generate a bivariate normal location-shift classification task
# containing 200 training objects and 200 to test with
class1 <- mvrnorm(150, c(0,0), 
                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
class2 <- mvrnorm(150, c(2,2), 
                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
propertyVars <- c(1:2)
classVar <- 3
data <- rbind(cbind(class1, rep(1, 150)), cbind(class2, rep(2, 150)))

# Train 1st DDalpha-classifier (default settings) 
# and get the classification error rate
stat <- ddalpha.getErrorRateCV(data, numchunks = 5)
cat("1. Classification error rate (defaults): ", 
    stat$error, ".\n", sep = "")

# Train 2nd DDalpha-classifier (zonoid depth, maximum Mahalanobis 
# depth classifier with defaults as outsider treatment) 
# and get the classification error rate
stat2 <- ddalpha.getErrorRateCV(data, depth = "zonoid", 
                          outsider.methods = "depth.Mahalanobis")
cat("2. Classification error rate (depth.Mahalanobis): ", 
    stat2$error, ".\n", sep = "")