evaluation.html

数据挖掘的最常用工具。由于开源

 Index of the class attribute (1, 2, ...; default: last). <p/>

 -x number of folds <br/>
 The number of folds for the cross-validation (default: 10). <p/>

 -no-cv <br/>
 No cross validation.  If no test file is provided, no evaluation
 is done. <p/>
 
 -split-percentage percentage <br/>
 Sets the percentage for the train/test set split, e.g., 66. <p/>
 
 -preserve-order <br/>
 Preserves the order in the percentage split instead of randomizing
 the data first with the seed value ('-s'). <p/>

 -s seed <br/>
 Random number seed for the cross-validation and percentage split
 (default: 1). <p/>

 -m file with cost matrix <br/>
 The name of a file containing a cost matrix. <p/>

 -l filename <br/>
 Loads classifier from the given file. In case the filename ends with
 ".xml" the options are loaded from XML. <p/>

 -d filename <br/>
 Saves classifier built from the training data into the given file. In case 
 the filename ends with ".xml" the options are saved XML, not the model. <p/>

 -v <br/>
 Outputs no statistics for the training data. <p/>

 -o <br/>
 Outputs statistics only, not the classifier. <p/>
 
 -i <br/>
 Outputs detailed information-retrieval statistics per class. <p/>

 -k <br/>
 Outputs information-theoretic statistics. <p/>

 -p range <br/>
 Outputs predictions for test instances (or the train instances if no test
 instances provided and -no-cv is used), along with the attributes in the specified range 
 (and nothing else). Use '-p 0' if no attributes are desired. <p/>

 -distribution <br/>
 Outputs the distribution instead of only the prediction
 in conjunction with the '-p' option (only nominal classes). <p/>

 -r <br/>
 Outputs cumulative margin distribution (and nothing else). <p/>

 -g <br/> 
 Only for classifiers that implement "Graphable." Outputs
 the graph representation of the classifier (and nothing
 else). <p/>

 -xml filename | xml-string <br/>
 Retrieves the options from the XML-data instead of the command line. <p/>
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classifier</CODE> - machine learning classifier<DD><CODE>options</CODE> - the array of string containing the options
<DT><B>Returns:</B><DD>a string describing the results
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated successfully</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateModel(weka.classifiers.Classifier, weka.core.Instances, java.lang.Object...)"><!-- --></A><H3>
evaluateModel</H3>
<PRE>
public double[] <B>evaluateModel</B>(<A HREF="../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A>&nbsp;classifier,
                              <A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;data,
                              java.lang.Object...&nbsp;forPredictionsPrinting)
                       throws java.lang.Exception</PRE>
<DL>
<DD>Evaluates the classifier on a given set of instances. Note that
 the data must have exactly the same format (e.g. order of
 attributes) as the data used to train the classifier! Otherwise
 the results will generally be meaningless.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classifier</CODE> - machine learning classifier<DD><CODE>data</CODE> - set of test instances for evaluation<DD><CODE>forPredictionsString</CODE> - varargs parameter that, if supplied, is
 expected to hold a StringBuffer to print predictions to, 
 a Range of attributes to output and a Boolean (true if the distribution
 is to be printed)
<DT><B>Returns:</B><DD>the predictions
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated 
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateModelOnceAndRecordPrediction(weka.classifiers.Classifier, weka.core.Instance)"><!-- --></A><H3>
evaluateModelOnceAndRecordPrediction</H3>
<PRE>
public double <B>evaluateModelOnceAndRecordPrediction</B>(<A HREF="../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A>&nbsp;classifier,
                                                   <A HREF="../../weka/core/Instance.html" title="class in weka.core">Instance</A>&nbsp;instance)
                                            throws java.lang.Exception</PRE>
<DL>
<DD>Evaluates the classifier on a single instance and records the
 prediction (if the class is nominal).
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classifier</CODE> - machine learning classifier<DD><CODE>instance</CODE> - the test instance to be classified
<DT><B>Returns:</B><DD>the prediction made by the clasifier
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated 
 successfully or the data contains string attributes</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateModelOnce(weka.classifiers.Classifier, weka.core.Instance)"><!-- --></A><H3>
evaluateModelOnce</H3>
<PRE>
public double <B>evaluateModelOnce</B>(<A HREF="../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A>&nbsp;classifier,
                                <A HREF="../../weka/core/Instance.html" title="class in weka.core">Instance</A>&nbsp;instance)
                         throws java.lang.Exception</PRE>
<DL>
<DD>Evaluates the classifier on a single instance.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classifier</CODE> - machine learning classifier<DD><CODE>instance</CODE> - the test instance to be classified
<DT><B>Returns:</B><DD>the prediction made by the clasifier
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated 
 successfully or the data contains string attributes</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateModelOnce(double[], weka.core.Instance)"><!-- --></A><H3>
evaluateModelOnce</H3>
<PRE>
public double <B>evaluateModelOnce</B>(double[]&nbsp;dist,
                                <A HREF="../../weka/core/Instance.html" title="class in weka.core">Instance</A>&nbsp;instance)
                         throws java.lang.Exception</PRE>
<DL>
<DD>Evaluates the supplied distribution on a single instance.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>dist</CODE> - the supplied distribution<DD><CODE>instance</CODE> - the test instance to be classified
<DT><B>Returns:</B><DD>the prediction
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated 
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateModelOnceAndRecordPrediction(double[], weka.core.Instance)"><!-- --></A><H3>
evaluateModelOnceAndRecordPrediction</H3>
<PRE>
public double <B>evaluateModelOnceAndRecordPrediction</B>(double[]&nbsp;dist,
                                                   <A HREF="../../weka/core/Instance.html" title="class in weka.core">Instance</A>&nbsp;instance)
                                            throws java.lang.Exception</PRE>
<DL>
<DD>Evaluates the supplied distribution on a single instance.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>dist</CODE> - the supplied distribution<DD><CODE>instance</CODE> - the test instance to be classified
<DT><B>Returns:</B><DD>the prediction
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated 
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateModelOnce(double, weka.core.Instance)"><!-- --></A><H3>
evaluateModelOnce</H3>
<PRE>
public void <B>evaluateModelOnce</B>(double&nbsp;prediction,
                              <A HREF="../../weka/core/Instance.html" title="class in weka.core">Instance</A>&nbsp;instance)
                       throws java.lang.Exception</PRE>
<DL>
<DD>Evaluates the supplied prediction on a single instance.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>prediction</CODE> - the supplied prediction<DD><CODE>instance</CODE> - the test instance to be classified
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated 
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="predictions()"><!-- --></A><H3>
predictions</H3>
<PRE>
public <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> <B>predictions</B>()</PRE>
<DL>
<DD>Returns the predictions that have been collected.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>a reference to the FastVector containing the predictions
 that have been collected. This should be null if no predictions
 have been collected (e.g. if the class is numeric).</DL>
</DD>
</DL>
<HR>

<A NAME="wekaStaticWrapper(weka.classifiers.Sourcable, java.lang.String)"><!-- --></A><H3>
wekaStaticWrapper</H3>
<PRE>
public static java.lang.String <B>wekaStaticWrapper</B>(<A HREF="../../weka/classifiers/Sourcable.html" title="interface in weka.classifiers">Sourcable</A>&nbsp;classifier,
                                                 java.lang.String&nbsp;className)
                                          throws java.lang.Exception</PRE>
<DL>
<DD>Wraps a static classifier in enough source to test using the weka
 class libraries.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classifier</CODE> - a Sourcable Classifier<DD><CODE>className</CODE> - the name to give to the source code class
<DT><B>Returns:</B><DD>the source for a static classifier that can be tested with
 weka libraries.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if code-generation fails</DL>
</DD>
</DL>
<HR>

<A NAME="numInstances()"><!-- --></A><H3>
numInstances</H3>
<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>