ddalpha: ddalpha.getErrorRatePart – R documentation

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ddalpha.getErrorRatePart

Test DD-Classifier

Description

Performs a benchmark procedure by partitioning the given data. On each of times steps size observations are removed from the data, the DD-classifier is trained on these data and tested on the removed observations.

Usage

ddalpha.getErrorRatePart(data, size = 0.3, times = 10,  ...)

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).
`size`	the excluded sequences size. Either an integer between 1 and n, or a fraction of data between 0 and 1.
`times`	the number of times the classifier is trained.
`...`	additional parameters passed to `ddalpha.train`

Value

`errors`	the part of incorrectly classified data (mean)
`errors_sd`	the standard deviation of errors
`errors_vec`	vector of errors
`time`	the mean training time
`time_sd`	the standard deviation of training time

Examples

# Generate a bivariate normal location-shift classification task
# containing 200 objects
class1 <- mvrnorm(100, c(0,0), 
                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
class2 <- mvrnorm(100, 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, 100)), cbind(class2, rep(2, 100)))

# Train 1st DDalpha-classifier (default settings) 
# and get the classification error rate
stat <- ddalpha.getErrorRatePart(data, size = 10, times = 10)
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.getErrorRatePart(data, depth = "zonoid", 
                                outsider.methods = "depth.Mahalanobis", size = 0.2, times = 10)
cat("2. Classification error rate (depth.Mahalanobis): ", 
    stat2$error, ".\n", sep = "")

ddalpha

Depth-Based Classification and Calculation of Data Depth

v1.3.11

GPL-2

Authors

Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]

Initial release

2020-01-09