Creates an object of class tune.control to be used with
the tune function, containing various control parameters.
tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = mean,
sampling = c("cross", "fix", "bootstrap"), sampling.aggregate = mean,
sampling.dispersion = sd,
cross = 10, fix = 2/3, nboot = 10, boot.size = 9/10, best.model = TRUE,
performances = TRUE, error.fun = NULL)if an integer value is specified, random
parameter vectors are drawn from the parameter space.
specifies how often training shall be repeated.
function for aggregating the repeated training results.
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 (with replacement) 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.
functions for aggregating the training results on the generated training samples (default: mean and standard deviation).
number of partitions for cross-validation.
part of the data used for training in fixed sampling.
number of bootstrap replications.
size of the bootstrap samples.
if TRUE, the best model is trained and
returned (the best parameter set is used for
training on the complete training set).
if TRUE, the performance results for all
parameter combinations are returned.
function returning the error measure to be minimized.
It takes two arguments: a vector of true values and a vector of
predicted values. If NULL, the misclassification error is used
for categorical predictions and the mean squared error for numeric
predictions.
An object of class "tune.control" containing all the above
parameters (either the defaults or the user specified values).