Convenience Tuning Wrapper Functions

Convenience tuning wrapper functions, using tune.

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.

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