tune.wrapper: Convenience Tuning Wrapper Functions

tune.wrapperR Documentation

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.gknn(x, y = NULL, data = NULL, k = NULL, ...)

best.gknn(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.


predicting function.


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

(g)knn parameters.

mtry, nodesize, ntree

randomForest parameters.

size, decay, trace

parameters passed to nnet.


object of class "tune.control" containing tuning parameters.


Further parameters passed to tune.


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

See Also


e1071 documentation built on Dec. 7, 2023, 8:15 p.m.