e1071 (version 1.3-16)

tune.wrapper: 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, ...)
best.svm(x, ...)
 
tune.nnet(x, y = NULL, data = NULL, size = NULL, decay = NULL, trace = FALSE, nrepeat = 5, 
          ...)
best.nnet(x, ...)
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, ...)
rpart.wrapper(formula, minsplit=20, minbucket=round(minsplit/3), cp=0.01, 
              maxcompete=4, maxsurrogate=5, usesurrogate=2, xval=10,
              surrogatestyle=0, maxdepth=30, ...)
tune.randomForest(x, y = NULL, data = NULL, nodesize = NULL, mtry = NULL, ntree = NULL, ...)
best.randomForest(x, ...)
tune.knn(x, y, k = NULL, l = NULL, ...) 
knn.wrapper(x, y, k = 1, l = 0, ...)

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

svm parameters.

k, l

knn parameters.

mtry, nodesize, ntree

randomForest parameters.

size, decay, nrepeat, trace

nnet 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