tune.wrapper: Convenience Tuning Wrapper Functions

In e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien

Convenience Tuning Wrapper Functions

Description

Convenience tuning wrapper functions, using tune.

Usage

tune.svm(x, y = NULL, data = NULL, degree = NULL, gamma = NULL, coef0 = NULL,
         cost = NULL, nu = NULL, class.weights = NULL, epsilon = NULL, ...)
best.svm(x, tunecontrol = tune.control(), ...)
 
tune.nnet(x, y = NULL, data = NULL, size = NULL, decay = NULL,
          trace = FALSE, tunecontrol = tune.control(nrepeat = 5), 
          ...)
best.nnet(x, tunecontrol = tune.control(nrepeat = 5), ...)

tune.rpart(formula, data, na.action = na.omit, minsplit = NULL,
           minbucket = NULL, cp = NULL, maxcompete = NULL, maxsurrogate = NULL,
           usesurrogate = NULL, xval = NULL, surrogatestyle = NULL, maxdepth =
           NULL, predict.func = NULL, ...)
best.rpart(formula, tunecontrol = tune.control(), ...)

tune.randomForest(x, y = NULL, data = NULL, nodesize = NULL, 
                  mtry = NULL, ntree = NULL, ...)
best.randomForest(x, tunecontrol = tune.control(), ...)

tune.gknn(x, y = NULL, data = NULL, k = NULL, ...)

best.gknn(x, tunecontrol = tune.control(), ...)

tune.knn(x, y, k = NULL, l = NULL, ...) 

Arguments

formula, x, y, data	formula and data arguments of function to be tuned.
predict.func	predicting function.
na.action	function handling missingness.
minsplit, minbucket, cp, maxcompete, maxsurrogate, usesurrogate, xval, surrogatestyle, maxdepth	rpart parameters.
degree, gamma, coef0, cost, nu, class.weights, epsilon	svm parameters.
k, l	(g)knn parameters.
mtry, nodesize, ntree	randomForest parameters.
size, decay, trace	parameters passed to nnet.
tunecontrol	object of class "tune.control" containing tuning parameters.
...	Further parameters passed to tune.

Details

For examples, see the help page of tune().

Value

tune.foo() returns a tuning object including the best parameter set obtained by optimizing over the specified parameter vectors. best.foo() directly returns the best model, i.e. the fit of a new model using the optimal parameters found by tune.foo.

Author(s)

David Meyer
[e-mail]

See Also

tune

e1071 documentation built on Sept. 17, 2024, 1:06 a.m.