Usage
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)
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 (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.
sampling.aggregate,sampling.dispersion
functions for aggregating the training
results on the generated training samples (default: mean and
standard deviation).
cross
number of partitions for cross-validation.
fix
part of the data used for training in fixed sampling.
nboot
number of bootstrap replications.
boot.size
size of the bootstrap samples.
best.model
if TRUE, the best model is trained and
returned (the best parameter set is used for
training on the complete training set).
performances
if TRUE, the performance results for all
parameter combinations are returned.
error.fun
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.