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, ...)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 |
|
degree, gamma, coef0, cost, nu, class.weights, epsilon |
|
k, l |
|
mtry, nodesize, ntree |
|
size, decay, trace |
parameters passed to
|
tunecontrol |
object of class |
... |
Further parameters passed to |
For examples, see the help page of tune().
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.
David Meyer
David.Meyer@R-project.org
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