thresholdcurve.html

这是一个由java实现的数据挖掘

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<A NAME="RELATION_NAME"><!-- --></A><H3>
RELATION_NAME</H3>
<PRE>
public static final java.lang.String <B>RELATION_NAME</B></PRE>
<DL>
<DD>The name of the relation used in threshold curve datasets</DL>
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<A NAME="TRUE_POS_NAME"><!-- --></A><H3>
TRUE_POS_NAME</H3>
<PRE>
public static final java.lang.String <B>TRUE_POS_NAME</B></PRE>
<DL>
</DL>
<HR>

<A NAME="FALSE_NEG_NAME"><!-- --></A><H3>
FALSE_NEG_NAME</H3>
<PRE>
public static final java.lang.String <B>FALSE_NEG_NAME</B></PRE>
<DL>
</DL>
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<A NAME="FALSE_POS_NAME"><!-- --></A><H3>
FALSE_POS_NAME</H3>
<PRE>
public static final java.lang.String <B>FALSE_POS_NAME</B></PRE>
<DL>
</DL>
<HR>

<A NAME="TRUE_NEG_NAME"><!-- --></A><H3>
TRUE_NEG_NAME</H3>
<PRE>
public static final java.lang.String <B>TRUE_NEG_NAME</B></PRE>
<DL>
</DL>
<HR>

<A NAME="FP_RATE_NAME"><!-- --></A><H3>
FP_RATE_NAME</H3>
<PRE>
public static final java.lang.String <B>FP_RATE_NAME</B></PRE>
<DL>
</DL>
<HR>

<A NAME="TP_RATE_NAME"><!-- --></A><H3>
TP_RATE_NAME</H3>
<PRE>
public static final java.lang.String <B>TP_RATE_NAME</B></PRE>
<DL>
</DL>
<HR>

<A NAME="PRECISION_NAME"><!-- --></A><H3>
PRECISION_NAME</H3>
<PRE>
public static final java.lang.String <B>PRECISION_NAME</B></PRE>
<DL>
</DL>
<HR>

<A NAME="RECALL_NAME"><!-- --></A><H3>
RECALL_NAME</H3>
<PRE>
public static final java.lang.String <B>RECALL_NAME</B></PRE>
<DL>
</DL>
<HR>

<A NAME="FALLOUT_NAME"><!-- --></A><H3>
FALLOUT_NAME</H3>
<PRE>
public static final java.lang.String <B>FALLOUT_NAME</B></PRE>
<DL>
</DL>
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<A NAME="FMEASURE_NAME"><!-- --></A><H3>
FMEASURE_NAME</H3>
<PRE>
public static final java.lang.String <B>FMEASURE_NAME</B></PRE>
<DL>
</DL>
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<A NAME="THRESHOLD_NAME"><!-- --></A><H3>
THRESHOLD_NAME</H3>
<PRE>
public static final java.lang.String <B>THRESHOLD_NAME</B></PRE>
<DL>
</DL>

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<A NAME="ThresholdCurve()"><!-- --></A><H3>
ThresholdCurve</H3>
<PRE>
public <B>ThresholdCurve</B>()</PRE>
<DL>
</DL>

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<A NAME="getCurve(weka.core.FastVector)"><!-- --></A><H3>
getCurve</H3>
<PRE>
public <A HREF="../../../weka/core/Instances.html">Instances</A> <B>getCurve</B>(<A HREF="../../../weka/core/FastVector.html">FastVector</A>&nbsp;predictions)</PRE>
<DL>
<DD>Calculates the performance stats for the default class and return 
 results as a set of Instances. The
 structure of these Instances is as follows:<p> <ul> 
 <li> <b>True Positives </b>
 <li> <b>False Negatives</b>
 <li> <b>False Positives</b>
 <li> <b>True Negatives</b>
 <li> <b>False Positive Rate</b>
 <li> <b>True Positive Rate</b>
 <li> <b>Precision</b>
 <li> <b>Recall</b>  
 <li> <b>Fallout</b>  
 <li> <b>Threshold</b> contains the probability threshold that gives
 rise to the previous performance values. 
 </ul> <p>
 For the definitions of these measures, see TwoClassStats <p><DD><DL>
<DT><B>Parameters:</B><DD><CODE>classIndex</CODE> - index of the class of interest.<DT><B>Returns:</B><DD>datapoints as a set of instances, null if no predictions
 have been made.<DT><B>See Also: </B><DD><A HREF="../../../weka/classifiers/evaluation/TwoClassStats.html"><CODE>TwoClassStats</CODE></A></DL>
</DD>
</DL>
<HR>

<A NAME="getCurve(weka.core.FastVector, int)"><!-- --></A><H3>
getCurve</H3>
<PRE>
public <A HREF="../../../weka/core/Instances.html">Instances</A> <B>getCurve</B>(<A HREF="../../../weka/core/FastVector.html">FastVector</A>&nbsp;predictions,
                          int&nbsp;classIndex)</PRE>
<DL>
<DD>Calculates the performance stats for the desired class and return 
 results as a set of Instances.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classIndex</CODE> - index of the class of interest.<DT><B>Returns:</B><DD>datapoints as a set of instances.</DL>
</DD>
</DL>
<HR>

<A NAME="getNPointPrecision(weka.core.Instances, int)"><!-- --></A><H3>
getNPointPrecision</H3>
<PRE>
public static double <B>getNPointPrecision</B>(<A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;tcurve,
                                        int&nbsp;n)</PRE>
<DL>
<DD>Calculates the n point precision result, which is the precision averaged
 over n evenly spaced (w.r.t recall) samples of the curve.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>tcurve</CODE> - a previously extracted threshold curve Instances.<DD><CODE>n</CODE> - the number of points to average over.<DT><B>Returns:</B><DD>the n-point precision.</DL>
</DD>
</DL>
<HR>

<A NAME="getROCArea(weka.core.Instances)"><!-- --></A><H3>
getROCArea</H3>
<PRE>
public static double <B>getROCArea</B>(<A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;tcurve)</PRE>
<DL>
<DD>Calculates the area under the ROC curve.  This is normalised so
 that 0.5 is random, 1.0 is perfect and 0.0 is bizarre.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>tcurve</CODE> - a previously extracted threshold curve Instances.<DT><B>Returns:</B><DD>the ROC area, or Double.NaN if you don't pass in 
 a ThresholdCurve generated Instances.</DL>
</DD>
</DL>
<HR>

<A NAME="getThresholdInstance(weka.core.Instances, double)"><!-- --></A><H3>
getThresholdInstance</H3>
<PRE>
public static int <B>getThresholdInstance</B>(<A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;tcurve,
                                       double&nbsp;threshold)</PRE>
<DL>
<DD>Gets the index of the instance with the closest threshold value to the
 desired target<DD><DL>
<DT><B>Parameters:</B><DD><CODE>tcurve</CODE> - a set of instances that have been generated by this class<DD><CODE>threshold</CODE> - the target threshold<DT><B>Returns:</B><DD>the index of the instance that has threshold closest to
 the target, or -1 if this could not be found (i.e. no data, or
 bad threshold target)</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>Tests the ThresholdCurve generation from the command line.
 The classifier is currently hardcoded. Pipe in an arff file.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>args</CODE> - currently ignored</DL>
</DD>
</DL>
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