tune {e1071}R Documentation

Parameter tuning of fuctions using grid search


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


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


method function to be tuned.
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 speciefied 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 behaviour 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.


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 conveneince, there are several tune.foo() wrappers defined, e.g., for nnet(), randomForest(), rpart(), svm(), and knn().


For tune, an object of class tune, including the components:

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.
if requested, the model trained on the complete training data using the best parameter combination.

best.tune returns the best model detected by tune.


David Meyer

See Also

tune.control, plot.tune, tune.svm, tune.wrapper


  ## 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))


  ## 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"))

  ## tune `rpart' for regression, using 10-fold cross validation (default)
  obj3 <- tune.rpart(mpg~., data = mtcars, minsplit = c(5,10,15))

  ## simple error estimation for lm using 10-fold cross validation
  tune(lm, mpg~., data = mtcars)

[Package e1071 version 1.5-2 Index]