e1071 (version 1.3-11)

tune.rpart: Convenience tuning functions

Description

Convenience tuning functions, using tune.

Usage

tune.svm(x, y = NULL, degree = NULL, gamma = NULL, coef0 = NULL, cost = NULL, 
         nu = NULL, ...)
best.svm(x, ...)
 
tune.nnet(x, y = NULL, size = NULL, decay = NULL, nrepeat = 5, trace = FALSE, 
          predict.func = function(...) predict(..., type = "class"), ...)
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, 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.

See Also

tune