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e1071 (version 1.5-15)

tune.wrapper: 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, ...)
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.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
svm parameters.
k, l
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.

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.

Details

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

See Also

tune