EVOLUTION-MANAGER
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Convenience Tuning Wrapper Functions</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for tune.wrapper {e1071}"><tr><td>tune.wrapper {e1071}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Convenience Tuning Wrapper Functions</h2> <h3>Description</h3> <p>Convenience tuning wrapper functions, using <code>tune</code>. </p> <h3>Usage</h3> <pre> 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, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>formula, x, y, data</code></td> <td> <p>formula and data arguments of function to be tuned.</p> </td></tr> <tr valign="top"><td><code>predict.func</code></td> <td> <p>predicting function.</p> </td></tr> <tr valign="top"><td><code>na.action</code></td> <td> <p>function handling missingness.</p> </td></tr> <tr valign="top"><td><code>minsplit, minbucket, cp, maxcompete, maxsurrogate, usesurrogate, xval, surrogatestyle, maxdepth</code></td> <td> <p><code>rpart</code> parameters.</p> </td></tr> <tr valign="top"><td><code>degree, gamma, coef0, cost, nu, class.weights, epsilon</code></td> <td> <p><code>svm</code> parameters.</p> </td></tr> <tr valign="top"><td><code>k, l</code></td> <td> <p><code>knn</code> parameters.</p> </td></tr> <tr valign="top"><td><code>mtry, nodesize, ntree</code></td> <td> <p><code>randomForest</code> parameters.</p> </td></tr> <tr valign="top"><td><code>size, decay, trace</code></td> <td> <p>parameters passed to <code>nnet</code>.</p> </td></tr> <tr valign="top"><td><code>tunecontrol</code></td> <td> <p>object of class <code>"tune.control"</code> containing tuning parameters.</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>Further parameters passed to <code>tune</code>.</p> </td></tr> </table> <h3>Details</h3> <p>For examples, see the help page of <code>tune()</code>.</p> <h3>Value</h3> <p><code>tune.foo()</code> returns a tuning object including the best parameter set obtained by optimizing over the specified parameter vectors. <code>best.foo()</code> directly returns the best model, i.e. the fit of a new model using the optimal parameters found by <code>tune.foo</code>. </p> <h3>Author(s)</h3> <p>David Meyer<br /> <a href="mailto:David.Meyer@R-project.org">David.Meyer@R-project.org</a> </p> <h3>See Also</h3> <p><code><a href="tune.html">tune</a></code></p> <hr /><div style="text-align: center;">[Package <em>e1071</em> version 1.7-3 <a href="00Index.html">Index</a>]</div> </body></html>