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.gknn(x, y = NULL, data = NULL, k = NULL, ...)
best.gknn(x, tunecontrol = tune.control(), ...)
tune.knn(x, y, k = NULL, l = NULL, ...)
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
.
formula and data arguments of function to be tuned.
predicting function.
function handling missingness.
rpart
parameters.
svm
parameters.
(g)knn
parameters.
randomForest
parameters.
parameters passed to
nnet
.
object of class "tune.control"
containing
tuning parameters.
Further parameters passed to tune
.
David Meyer
David.Meyer@R-project.org
For examples, see the help page of tune()
.
tune