📄 evaluation.html
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</TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#fMeasure(int)">fMeasure</A></B>(int classIndex)</CODE><BR> Calculate the F-Measure with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#incorrect()">incorrect</A></B>()</CODE><BR> Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#kappa()">kappa</A></B>()</CODE><BR> Returns value of kappa statistic if class is nominal.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#KBInformation()">KBInformation</A></B>()</CODE><BR> Return the total Kononenko & Bratko Information score in bits</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#KBMeanInformation()">KBMeanInformation</A></B>()</CODE><BR> Return the Kononenko & Bratko Information score in bits per instance.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#KBRelativeInformation()">KBRelativeInformation</A></B>()</CODE><BR> Return the Kononenko & Bratko Relative Information score</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>static void</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#main(java.lang.String[])">main</A></B>(java.lang.String[] args)</CODE><BR> A test method for this class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#meanAbsoluteError()">meanAbsoluteError</A></B>()</CODE><BR> Returns the mean absolute error.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#meanPriorAbsoluteError()">meanPriorAbsoluteError</A></B>()</CODE><BR> Returns the mean absolute error of the prior.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#numFalseNegatives(int)">numFalseNegatives</A></B>(int classIndex)</CODE><BR> Calculate number of false negatives with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#numFalsePositives(int)">numFalsePositives</A></B>(int classIndex)</CODE><BR> Calculate number of false positives with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#numInstances()">numInstances</A></B>()</CODE><BR> 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).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#numTrueNegatives(int)">numTrueNegatives</A></B>(int classIndex)</CODE><BR> Calculate the number of true negatives with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#numTruePositives(int)">numTruePositives</A></B>(int classIndex)</CODE><BR> Calculate the number of true positives with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#pctCorrect()">pctCorrect</A></B>()</CODE><BR> Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#pctIncorrect()">pctIncorrect</A></B>()</CODE><BR> Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#pctUnclassified()">pctUnclassified</A></B>()</CODE><BR> Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#precision(int)">precision</A></B>(int classIndex)</CODE><BR> Calculate the precision with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#priorEntropy()">priorEntropy</A></B>()</CODE><BR> Calculate the entropy of the prior distribution</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#recall(int)">recall</A></B>(int classIndex)</CODE><BR> Calculate the recall with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#relativeAbsoluteError()">relativeAbsoluteError</A></B>()</CODE><BR> Returns the relative absolute error.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#rootMeanPriorSquaredError()">rootMeanPriorSquaredError</A></B>()</CODE><BR> Returns the root mean prior squared error.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#rootMeanSquaredError()">rootMeanSquaredError</A></B>()</CODE><BR> Returns the root mean squared error.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#rootRelativeSquaredError()">rootRelativeSquaredError</A></B>()</CODE><BR> Returns the root relative squared error if the class is numeric.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#setPriors(weka.core.Instances)">setPriors</A></B>(<A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A> train)</CODE><BR> Sets the class prior probabilities</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#SFEntropyGain()">SFEntropyGain</A></B>()</CODE><BR> Returns the total SF, which is the null model entropy minus the scheme entropy.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#SFMeanEntropyGain()">SFMeanEntropyGain</A></B>()</CODE><BR> Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#SFMeanPriorEntropy()">SFMeanPriorEntropy</A></B>()</CODE><BR> Returns the entropy per instance for the null model</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#SFMeanSchemeEntropy()">SFMeanSchemeEntropy</A></B>()</CODE><BR> Returns the entropy per instance for the scheme</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#SFPriorEntropy()">SFPriorEntropy</A></B>()</CODE><BR> Returns the total entropy for the null model</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#SFSchemeEntropy()">SFSchemeEntropy</A></B>()</CODE><BR> Returns the total entropy for the scheme</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toClassDetailsString()">toClassDetailsString</A></B>()</CODE><BR> </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toClassDetailsString(java.lang.String)">toClassDetailsString</A></B>(java.lang.String title)</CODE><BR> Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toCumulativeMarginDistributionString()">toCumulativeMarginDistributionString</A></B>()</CODE><BR> Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toMatrixString()">toMatrixString</A></B>()</CODE><BR> Calls toMatrixString() with a default title.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toMatrixString(java.lang.String)">toMatrixString</A></B>(java.lang.String title)</CODE><BR> Outputs the performance statistics as a classification confusion matrix.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toSummaryString()">toSummaryString</A></B>()</CODE><BR> Calls toSummaryString() with no title and no complexity stats</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toSummaryString(boolean)">toSummaryString</A></B>(boolean printComplexityStatistics)</CODE><BR> Calls toSummaryString() with a default title.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#toSummaryString(java.lang.String, boolean)">toSummaryString</A></B>(java.lang.String title, boolean printComplexityStatistics)</CODE><BR> Outputs the performance statistics in summary form.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#totalCost()">totalCost</A></B>()</CODE><BR> Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#trueNegativeRate(int)">trueNegativeRate</A></B>(int classIndex)</CODE><BR> Calculate the true negative rate with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#truePositiveRate(int)">truePositiveRate</A></B>(int classIndex)</CODE><BR> Calculate the true positive rate with respect to a particular class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#unclassified()">unclassified</A></B>()</CODE><BR> Gets the number of instances not classified (that is, for which no prediction was made by the classifier).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/Evaluation.html#updatePriors(weka.core.Instance)">updatePriors</A></B>(<A HREF="../../weka/core/Instance.html" title="class in weka.core">Instance</A> instance)</CODE><BR> Updates the class prior probabilities (when incrementally training)</TD></TR></TABLE> <A NAME="methods_inherited_from_class_java.lang.Object"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TD><B>Methods inherited from class java.lang.Object</B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE>getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</CODE></TD></TR>⌨️ 快捷键说明
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