Last data update: 2014.03.03
R: Convenience Tuning Wrapper Functions
tune.wrapper 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
Results