tune.wrapper {e1071}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, ...)
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(), ...)
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, tunecontrol = tune.control(), ...)

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, 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@ci.tuwien.ac.at

See Also

tune


[Package e1071 version 1.5-2 Index]