tune.control {e1071}R Documentation

Control Parameters for the Tune Function

Description

Creates an object of class tune.control to be used with the tune function, containing various control parameters.

Usage

tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min,
sampling = c("cross", "fix", "bootstrap"), sampling.aggregate = mean,
cross = 10, fix = 2/3, nboot = 10, boot.size = 9/10, best.model = TRUE,
performances = TRUE)

Arguments

random if an integer value is specified, random parameter vectors are drawn from the parameter space.
nrepeat specifies how often training shall be repeated.
repeat.aggregate function for aggregating the repeated training results.
sampling sampling scheme. If sampling = "cross", a cross-times cross validation is performed. If sampling = "boot", nboot training sets of size boot.size (part) are sampled from the supplied data. If sampling = "fix", a single split into training/validation set is used, the training set containing a fix part of the supplied data. Note that a separate validation set can be supplied via validation.x and validation.y. It is only used for sampling = "boot" and sampling = "fix"; in the latter case, fix is set to 1.
sampling.aggregate function for aggregating the training results on the generated training samples.
cross number of partitions for cross-validation.
fix part of the data used for training in fixed sampling.
nboot number of bootstrap replications.
bthe supplied datting function svm.default
subset An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
na.action A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

Details

For multiclass-classification with k levels, k>2, libsvm uses the ‘one-against-one’-approach, in which k(k-1)/2 binary classifiers are trained; the appropriate class is found by a voting scheme.