classification

Given the predicted value <code>q</code> and the observed classes <code>y</code>,
it shows an overview of the prediction performances with hit rates,
misclassification rates, true positives (TP), false positives (FP)
and precision.

Implements the SVM-Maj algorithm to train data with support vector machine
<doi:10.1007/s11634-008-0020-9>.
This algorithm uses two efficient updates, one for linear kernel and one
for the nonlinear kernel.

Hok San Yip

SVMMaj

Implementation of the SVM-Maj Algorithm

Hoksan Yip

Georgi Nalbantov

classification function

<dl><dt>q</dt>
<dd>the predicted values</dd>
<dt>y</dt>
<dd>a list of the actual classes of <code>q</code></dd>
<dt>classes</dt>
<dd>a character vector with the labels of the two classes</dd>
<dt>weights</dt>
<dd>an optional parameter to specify a weighted hit rate and
misclassification rate</dd></dl>

Arguments

Show the classification performance — classification

<dl>

<dt>q</dt>
<dd>the predicted values</dd>


<dt>y</dt>
<dd>a list of the actual classes of <code>q</code></dd>


<dt>classes</dt>
<dd>a character vector with the labels of the two classes</dd>


<dt>weights</dt>
<dd>an optional parameter to specify a weighted hit rate and
misclassification rate</dd>

</dl>

classification: Show the classification performance

Description

Usage

Value

Arguments