evaluation.html

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<PRE>
public final double <B>numInstances</B>()</PRE>
<DL>
<DD>Gets the number of test instances that had a known class value
 (actually the sum of the weights of test instances with known 
 class value).
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the number of test instances with known class</DL>
</DD>
</DL>
<HR>

<A NAME="incorrect()"><!-- --></A><H3>
incorrect</H3>
<PRE>
public final double <B>incorrect</B>()</PRE>
<DL>
<DD>Gets the number of instances incorrectly classified (that is, for
 which an incorrect prediction was made). (Actually the sum of the weights
 of these instances)
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the number of incorrectly classified instances</DL>
</DD>
</DL>
<HR>

<A NAME="pctIncorrect()"><!-- --></A><H3>
pctIncorrect</H3>
<PRE>
public final double <B>pctIncorrect</B>()</PRE>
<DL>
<DD>Gets the percentage of instances incorrectly classified (that is, for
 which an incorrect prediction was made).
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the percent of incorrectly classified instances 
 (between 0 and 100)</DL>
</DD>
</DL>
<HR>

<A NAME="totalCost()"><!-- --></A><H3>
totalCost</H3>
<PRE>
public final double <B>totalCost</B>()</PRE>
<DL>
<DD>Gets the total cost, that is, the cost of each prediction times the
 weight of the instance, summed over all instances.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the total cost</DL>
</DD>
</DL>
<HR>

<A NAME="avgCost()"><!-- --></A><H3>
avgCost</H3>
<PRE>
public final double <B>avgCost</B>()</PRE>
<DL>
<DD>Gets the average cost, that is, total cost of misclassifications
 (incorrect plus unclassified) over the total number of instances.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the average cost.</DL>
</DD>
</DL>
<HR>

<A NAME="correct()"><!-- --></A><H3>
correct</H3>
<PRE>
public final double <B>correct</B>()</PRE>
<DL>
<DD>Gets the number of instances correctly classified (that is, for
 which a correct prediction was made). (Actually the sum of the weights
 of these instances)
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the number of correctly classified instances</DL>
</DD>
</DL>
<HR>

<A NAME="pctCorrect()"><!-- --></A><H3>
pctCorrect</H3>
<PRE>
public final double <B>pctCorrect</B>()</PRE>
<DL>
<DD>Gets the percentage of instances correctly classified (that is, for
 which a correct prediction was made).
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the percent of correctly classified instances (between 0 and 100)</DL>
</DD>
</DL>
<HR>

<A NAME="unclassified()"><!-- --></A><H3>
unclassified</H3>
<PRE>
public final double <B>unclassified</B>()</PRE>
<DL>
<DD>Gets the number of instances not classified (that is, for
 which no prediction was made by the classifier). (Actually the sum
 of the weights of these instances)
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the number of unclassified instances</DL>
</DD>
</DL>
<HR>

<A NAME="pctUnclassified()"><!-- --></A><H3>
pctUnclassified</H3>
<PRE>
public final double <B>pctUnclassified</B>()</PRE>
<DL>
<DD>Gets the percentage of instances not classified (that is, for
 which no prediction was made by the classifier).
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the percent of unclassified instances (between 0 and 100)</DL>
</DD>
</DL>
<HR>

<A NAME="errorRate()"><!-- --></A><H3>
errorRate</H3>
<PRE>
public final double <B>errorRate</B>()</PRE>
<DL>
<DD>Returns the estimated error rate or the root mean squared error
 (if the class is numeric). If a cost matrix was given this
 error rate gives the average cost.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the estimated error rate (between 0 and 1, or between 0 and 
 maximum cost)</DL>
</DD>
</DL>
<HR>

<A NAME="kappa()"><!-- --></A><H3>
kappa</H3>
<PRE>
public final double <B>kappa</B>()</PRE>
<DL>
<DD>Returns value of kappa statistic if class is nominal.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the value of the kappa statistic</DL>
</DD>
</DL>
<HR>

<A NAME="correlationCoefficient()"><!-- --></A><H3>
correlationCoefficient</H3>
<PRE>
public final double <B>correlationCoefficient</B>()
                                    throws java.lang.Exception</PRE>
<DL>
<DD>Returns the correlation coefficient if the class is numeric.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the correlation coefficient
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if class is not numeric</DL>
</DD>
</DL>
<HR>

<A NAME="meanAbsoluteError()"><!-- --></A><H3>
meanAbsoluteError</H3>
<PRE>
public final double <B>meanAbsoluteError</B>()</PRE>
<DL>
<DD>Returns the mean absolute error. Refers to the error of the
 predicted values for numeric classes, and the error of the 
 predicted probability distribution for nominal classes.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

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

<A NAME="meanPriorAbsoluteError()"><!-- --></A><H3>
meanPriorAbsoluteError</H3>
<PRE>
public final double <B>meanPriorAbsoluteError</B>()</PRE>
<DL>
<DD>Returns the mean absolute error of the prior.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

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

<A NAME="relativeAbsoluteError()"><!-- --></A><H3>
relativeAbsoluteError</H3>
<PRE>
public final double <B>relativeAbsoluteError</B>()
                                   throws java.lang.Exception</PRE>
<DL>
<DD>Returns the relative absolute error.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the relative absolute error
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if it can't be computed</DL>
</DD>
</DL>
<HR>

<A NAME="rootMeanSquaredError()"><!-- --></A><H3>
rootMeanSquaredError</H3>
<PRE>
public final double <B>rootMeanSquaredError</B>()</PRE>
<DL>
<DD>Returns the root mean squared error.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the root mean squared error</DL>
</DD>
</DL>
<HR>

<A NAME="rootMeanPriorSquaredError()"><!-- --></A><H3>
rootMeanPriorSquaredError</H3>
<PRE>
public final double <B>rootMeanPriorSquaredError</B>()</PRE>
<DL>
<DD>Returns the root mean prior squared error.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the root mean prior squared error</DL>
</DD>
</DL>
<HR>

<A NAME="rootRelativeSquaredError()"><!-- --></A><H3>
rootRelativeSquaredError</H3>
<PRE>
public final double <B>rootRelativeSquaredError</B>()</PRE>
<DL>
<DD>Returns the root relative squared error if the class is numeric.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the root relative squared error</DL>
</DD>
</DL>
<HR>

<A NAME="priorEntropy()"><!-- --></A><H3>
priorEntropy</H3>
<PRE>
public final double <B>priorEntropy</B>()
                          throws java.lang.Exception</PRE>
<DL>
<DD>Calculate the entropy of the prior distribution
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the entropy of the prior distribution
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if the class is not nominal</DL>
</DD>
</DL>
<HR>

<A NAME="KBInformation()"><!-- --></A><H3>
KBInformation</H3>
<PRE>
public final double <B>KBInformation</B>()
                           throws java.lang.Exception</PRE>
<DL>
<DD>Return the total Kononenko & Bratko Information score in bits
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the K&B information score
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if the class is not nominal</DL>
</DD>
</DL>
<HR>

<A NAME="KBMeanInformation()"><!-- --></A><H3>
KBMeanInformation</H3>
<PRE>
public final double <B>KBMeanInformation</B>()
                               throws java.lang.Exception</PRE>
<DL>
<DD>Return the Kononenko & Bratko Information score in bits per 
 instance.
<P>
<DD><DL>