R: Convenience Tuning Wrapper Functions

tune.wrapper {e1071}

R Documentation

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
David.Meyer@R-project.org