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.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` | `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

### See Also

`tune`

[Package e1071 version 1.7-6 Index]