Control Parameters for the Tune Function
Creates an object of class
tune.control to be used with
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,
randomparameter 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",
nboottraining 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
fixpart of the supplied data. Note that a separate validation set can be supplied via
validation.y. It is only used for
sampling = "boot"and
sampling = "fix"; in the latter case,
fixis 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.
TRUE, the best model is trained and returned (the best parameter set is used for training on the complete training set).
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).