twoclassstats.html

< 数据挖掘--实用机器学习技术及java实现> 一书结合数据挖掘和机器学习的知识,作者陈述了自动挖掘模式的基础理论,并且以java语言实现了具有代表性的各类数据挖掘方法.例如:class

<A NAME="TwoClassStats(double, double, double, double)"><!-- --></A><H3>
TwoClassStats</H3>
<PRE>
public <B>TwoClassStats</B>(double&nbsp;tp,
                     double&nbsp;fp,
                     double&nbsp;tn,
                     double&nbsp;fn)</PRE>
<DL>
<DD>Creates the TwoClassStats with the given initial performance values.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>tp</CODE> - the number of correctly classified positives<DD><CODE>fp</CODE> - the number of incorrectly classified negatives<DD><CODE>tn</CODE> - the number of correctly classified negatives<DD><CODE>fn</CODE> - the number of incorrectly classified positives</DL>
</DD>
</DL>

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<B>Method Detail</B></FONT></TD>
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<A NAME="setTruePositive(double)"><!-- --></A><H3>
setTruePositive</H3>
<PRE>
public void <B>setTruePositive</B>(double&nbsp;tp)</PRE>
<DL>
<DD>Sets the number of positive instances predicted as positive</DL>
<HR>

<A NAME="setFalsePositive(double)"><!-- --></A><H3>
setFalsePositive</H3>
<PRE>
public void <B>setFalsePositive</B>(double&nbsp;fp)</PRE>
<DL>
<DD>Sets the number of negative instances predicted as positive</DL>
<HR>

<A NAME="setTrueNegative(double)"><!-- --></A><H3>
setTrueNegative</H3>
<PRE>
public void <B>setTrueNegative</B>(double&nbsp;tn)</PRE>
<DL>
<DD>Sets the number of negative instances predicted as negative</DL>
<HR>

<A NAME="setFalseNegative(double)"><!-- --></A><H3>
setFalseNegative</H3>
<PRE>
public void <B>setFalseNegative</B>(double&nbsp;fn)</PRE>
<DL>
<DD>Sets the number of positive instances predicted as negative</DL>
<HR>

<A NAME="getTruePositive()"><!-- --></A><H3>
getTruePositive</H3>
<PRE>
public double <B>getTruePositive</B>()</PRE>
<DL>
<DD>Gets the number of positive instances predicted as positive</DL>
<HR>

<A NAME="getFalsePositive()"><!-- --></A><H3>
getFalsePositive</H3>
<PRE>
public double <B>getFalsePositive</B>()</PRE>
<DL>
<DD>Gets the number of negative instances predicted as positive</DL>
<HR>

<A NAME="getTrueNegative()"><!-- --></A><H3>
getTrueNegative</H3>
<PRE>
public double <B>getTrueNegative</B>()</PRE>
<DL>
<DD>Gets the number of negative instances predicted as negative</DL>
<HR>

<A NAME="getFalseNegative()"><!-- --></A><H3>
getFalseNegative</H3>
<PRE>
public double <B>getFalseNegative</B>()</PRE>
<DL>
<DD>Gets the number of positive instances predicted as negative</DL>
<HR>

<A NAME="getTruePositiveRate()"><!-- --></A><H3>
getTruePositiveRate</H3>
<PRE>
public double <B>getTruePositiveRate</B>()</PRE>
<DL>
<DD>Calculate the true positive rate. 
 This is defined as<p>
 <pre>
 correctly classified positives
 ------------------------------
       total positives
 </pre><DD><DL>
<DT><B>Returns:</B><DD>the true positive rate</DL>
</DD>
</DL>
<HR>

<A NAME="getFalsePositiveRate()"><!-- --></A><H3>
getFalsePositiveRate</H3>
<PRE>
public double <B>getFalsePositiveRate</B>()</PRE>
<DL>
<DD>Calculate the false positive rate. 
 This is defined as<p>
 <pre>
 incorrectly classified negatives
 --------------------------------
        total negatives
 </pre><DD><DL>
<DT><B>Returns:</B><DD>the false positive rate</DL>
</DD>
</DL>
<HR>

<A NAME="getPrecision()"><!-- --></A><H3>
getPrecision</H3>
<PRE>
public double <B>getPrecision</B>()</PRE>
<DL>
<DD>Calculate the precision. 
 This is defined as<p>
 <pre>
 correctly classified positives
 ------------------------------
  total predicted as positive
 </pre><DD><DL>
<DT><B>Returns:</B><DD>the precision</DL>
</DD>
</DL>
<HR>

<A NAME="getRecall()"><!-- --></A><H3>
getRecall</H3>
<PRE>
public double <B>getRecall</B>()</PRE>
<DL>
<DD>Calculate the recall. 
 This is defined as<p>
 <pre>
 correctly classified positives
 ------------------------------
       total positives
 </pre><p>
 (Which is also the same as the truePositiveRate.)<DD><DL>
<DT><B>Returns:</B><DD>the recall</DL>
</DD>
</DL>
<HR>

<A NAME="getFMeasure()"><!-- --></A><H3>
getFMeasure</H3>
<PRE>
public double <B>getFMeasure</B>()</PRE>
<DL>
<DD>Calculate the F-Measure. 
 This is defined as<p>
 <pre>
 2 * recall * precision
 ----------------------
   recall + precision
 </pre><DD><DL>
<DT><B>Returns:</B><DD>the F-Measure</DL>
</DD>
</DL>
<HR>

<A NAME="getFallout()"><!-- --></A><H3>
getFallout</H3>
<PRE>
public double <B>getFallout</B>()</PRE>
<DL>
<DD>Calculate the fallout. 
 This is defined as<p>
 <pre>
 incorrectly classified negatives
 --------------------------------
   total predicted as positive
 </pre><DD><DL>
<DT><B>Returns:</B><DD>the fallout</DL>
</DD>
</DL>
<HR>

<A NAME="getConfusionMatrix()"><!-- --></A><H3>
getConfusionMatrix</H3>
<PRE>
public <A HREF="../../../weka/classifiers/evaluation/ConfusionMatrix.html">ConfusionMatrix</A> <B>getConfusionMatrix</B>()</PRE>
<DL>
<DD>Generates a <code>ConfusionMatrix</code> representing the current
 two-class statistics, using class names "negative" and "positive".<DD><DL>
<DT><B>Returns:</B><DD>a <code>ConfusionMatrix</code>.</DL>
</DD>
</DL>
<HR>

<A NAME="toString()"><!-- --></A><H3>
toString</H3>
<PRE>
public java.lang.String <B>toString</B>()</PRE>
<DL>
<DD>Returns a string containing the various performance measures
 for the current object<DD><DL>
<DT><B>Overrides:</B><DD><CODE>toString</CODE> in class <CODE>java.lang.Object</CODE></DL>
</DD>
</DL>
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