bvdecomposesegcvsub.html

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<DT><B>Returns:</B><DD>the index of the class attribute</DL>
</DD>
</DL>
<HR>

<A NAME="setClassIndex(int)"><!-- --></A><H3>
setClassIndex</H3>
<PRE>
public void <B>setClassIndex</B>(int&nbsp;classIndex)</PRE>
<DL>
<DD>Sets index of attribute to discretize on
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classIndex</CODE> - the index (starting from 1) of the class attribute</DL>
</DD>
</DL>
<HR>

<A NAME="getKWBias()"><!-- --></A><H3>
getKWBias</H3>
<PRE>
public double <B>getKWBias</B>()</PRE>
<DL>
<DD>Get the calculated bias squared according to the Kohavi and Wolpert definition
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the bias squared</DL>
</DD>
</DL>
<HR>

<A NAME="getWBias()"><!-- --></A><H3>
getWBias</H3>
<PRE>
public double <B>getWBias</B>()</PRE>
<DL>
<DD>Get the calculated bias according to the Webb definition
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the bias</DL>
</DD>
</DL>
<HR>

<A NAME="getKWVariance()"><!-- --></A><H3>
getKWVariance</H3>
<PRE>
public double <B>getKWVariance</B>()</PRE>
<DL>
<DD>Get the calculated variance according to the Kohavi and Wolpert definition
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the variance</DL>
</DD>
</DL>
<HR>

<A NAME="getWVariance()"><!-- --></A><H3>
getWVariance</H3>
<PRE>
public double <B>getWVariance</B>()</PRE>
<DL>
<DD>Get the calculated variance according to the Webb definition
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD></DL>
</DD>
</DL>
<HR>

<A NAME="getKWSigma()"><!-- --></A><H3>
getKWSigma</H3>
<PRE>
public double <B>getKWSigma</B>()</PRE>
<DL>
<DD>Get the calculated sigma according to the Kohavi and Wolpert definition
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the sigma</DL>
</DD>
</DL>
<HR>

<A NAME="setTrainSize(int)"><!-- --></A><H3>
setTrainSize</H3>
<PRE>
public void <B>setTrainSize</B>(int&nbsp;size)</PRE>
<DL>
<DD>Set the training size.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>size</CODE> - the size of the training set</DL>
</DD>
</DL>
<HR>

<A NAME="getTrainSize()"><!-- --></A><H3>
getTrainSize</H3>
<PRE>
public int <B>getTrainSize</B>()</PRE>
<DL>
<DD>Get the training size
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the size of the training set</DL>
</DD>
</DL>
<HR>

<A NAME="setP(double)"><!-- --></A><H3>
setP</H3>
<PRE>
public void <B>setP</B>(double&nbsp;proportion)</PRE>
<DL>
<DD>Set the proportion of instances that are common between two training sets
 used to train a classifier.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>proportion</CODE> - the proportion of instances that are common between training
 sets.</DL>
</DD>
</DL>
<HR>

<A NAME="getP()"><!-- --></A><H3>
getP</H3>
<PRE>
public double <B>getP</B>()</PRE>
<DL>
<DD>Get the proportion of instances that are common between two training sets.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the proportion</DL>
</DD>
</DL>
<HR>

<A NAME="getError()"><!-- --></A><H3>
getError</H3>
<PRE>
public double <B>getError</B>()</PRE>
<DL>
<DD>Get the calculated error rate
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the error rate</DL>
</DD>
</DL>
<HR>

<A NAME="decompose()"><!-- --></A><H3>
decompose</H3>
<PRE>
public void <B>decompose</B>()
               throws java.lang.Exception</PRE>
<DL>
<DD>Carry out the bias-variance decomposition using the sub-sampled cross-validation method.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if the decomposition couldn't be carried out</DL>
</DD>
</DL>
<HR>

<A NAME="findCentralTendencies(double[])"><!-- --></A><H3>
findCentralTendencies</H3>
<PRE>
public java.util.Vector <B>findCentralTendencies</B>(double[]&nbsp;predProbs)</PRE>
<DL>
<DD>Finds the central tendency, given the classifications for an instance.

 Where the central tendency is defined as the class that was most commonly
 selected for a given instance.<p>

 For example, instance 'x' may be classified out of 3 classes y = {1, 2, 3},
 so if x is classified 10 times, and is classified as follows, '1' = 2 times, '2' = 5 times
 and '3' = 3 times. Then the central tendency is '2'. <p>

 However, it is important to note that this method returns a list of all classes
 that have the highest number of classifications.

 In cases where there are several classes with the largest number of classifications, then
 all of these classes are returned. For example if 'x' is classified '1' = 4 times,
 '2' = 4 times and '3' = 2 times. Then '1' and '2' are returned.<p>
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>predProbs</CODE> - the array of classifications for a single instance.
<DT><B>Returns:</B><DD>a Vector containing Integer objects which store the class(s) which
 are the central tendency.</DL>
</DD>
</DL>
<HR>

<A NAME="toString()"><!-- --></A><H3>
toString</H3>
<PRE>
public java.lang.String <B>toString</B>()</PRE>
<DL>
<DD>Returns description of the bias-variance decomposition results.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the bias-variance decomposition results as a string</DL>
</DD>
</DL>
<HR>

<A NAME="main(java.lang.String[])"><!-- --></A><H3>
main</H3>
<PRE>
public static void <B>main</B>(java.lang.String[]&nbsp;args)</PRE>
<DL>
<DD>Test method for this class
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>args</CODE> - the command line arguments</DL>
</DD>
</DL>
<HR>

<A NAME="randomize(int[], java.util.Random)"><!-- --></A><H3>
randomize</H3>
<PRE>
public final void <B>randomize</B>(int[]&nbsp;index,
                            java.util.Random&nbsp;random)</PRE>
<DL>
<DD>Accepts an array of ints and randomises the values in the array, using the
 random seed.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>index</CODE> - is the array of integers<DD><CODE>random</CODE> - is the Random seed.</DL>
</DD>
</DL>
<!-- ========= END OF CLASS DATA ========= -->
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