# tune {e1071}

### Description

This generic function tunes hyperparameters of statistical methods using a grid search over supplied parameter ranges.

### Usage

tune(method, train.x, train.y = NULL, data = list(), validation.x = NULL, validation.y = NULL, ranges = NULL, predict.func = predict, tunecontrol = tune.control(), ...) best.tune(...)

### Arguments

- method
- either the function to be tuned, or a character string naming such a function.
- train.x
- either a formula or a matrix of predictors.
- train.y
- the response variable if
`train.x`

is a predictor matrix. Ignored if`train.x`

is a formula. - data
- data, if a formula interface is used. Ignored, if predictor matrix and response are supplied directly.
- validation.x
- an optional validation set. Depending on whether a formula interface is used or not, the response can be included in
`validation.x`

or separately specified using`validation.y`

. - validation.y
- if no formula interface is used, the response of the (optional) validation set.
- ranges
- a named list of parameter vectors spanning the sampling space. The vectors will usually be created by
`seq`

. - predict.func
- optional predict function, if the standard
`predict`

behavior is inadequate. - tunecontrol
- object of class
`"tune.control"`

, as created by the function`tune.control()`

. If omitted,`tune.control()`

gives the defaults. - ...
- Further parameters passed to the training functions.

### Details

As performance measure, the classification error is used for classification, and the mean squared error for regression. It is possible to specify only one parameter combination (i.e., vectors of length 1) to obtain an error estimation of the specified type (bootstrap, cross-classification, etc.) on the given data set. For convenience, there are several `tune.foo()`

wrappers defined, e.g., for `nnet()`

, `randomForest()`

, `rpart()`

, `svm()`

, and `knn()`

.

Cross-validation randomizes the data set before building the splits which---once created---remain constant during the training process. The splits can be recovered through the `train.ind`

component of the returned object.

### Values

For `tune`

, an object of class `tune`

, including the components: `best.tune()`

returns the best model detected by `tune`

.

- best.parameters
- a 1 x k data frame, k number of parameters.
- best.performance
- best achieved performance.
- performances
- if requested, a data frame of all parameter combinations along with the corresponding performance results.
- train.ind
- list of index vectors used for splits into training and validation sets.
- best.model
- if requested, the model trained on the complete training data using the best parameter combination.

### See Also

`tune.control`

, `plot.tune`

, `tune.svm`

, tune.wrapper

### Examples

data(iris) ## tune `svm' for classification with RBF-kernel (default in svm), ## using one split for training/validation set obj <- tune(svm, Species~., data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)), tunecontrol = tune.control(sampling = "fix") ) ## alternatively: ## obj <- tune.svm(Species~., data = iris, gamma = 2^(-1:1), cost = 2^(2:4)) summary(obj) plot(obj) ## tune `knn' using a convenience function; this time with the ## conventional interface and bootstrap sampling: x <- iris[,-5] y <- iris[,5] obj2 <- tune.knn(x, y, k = 1:5, tunecontrol = tune.control(sampling = "boot")) summary(obj2) plot(obj2) ## tune `rpart' for regression, using 10-fold cross validation (default) data(mtcars) obj3 <- tune.rpart(mpg~., data = mtcars, minsplit = c(5,10,15)) summary(obj3) plot(obj3) ## simple error estimation for lm using 10-fold cross validation tune(lm, mpg~., data = mtcars)

Documentation reproduced from package e1071, version 1.6-2. License: GPL-2