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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Frameset//EN""http://www.w3.org/TR/REC-html40/frameset.dtd"><!--NewPage--><HTML><HEAD><!-- Generated by javadoc on Wed Sep 04 10:31:48 CDT 2002 --><TITLE>: Class AdditiveRegression</TITLE><LINK REL ="stylesheet" TYPE="text/css" HREF="../../stylesheet.css" TITLE="Style"></HEAD><BODY BGCOLOR="white"><!-- ========== START OF NAVBAR ========== --><A NAME="navbar_top"><!-- --></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0"><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_top_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3"> <TR ALIGN="center" VALIGN="top"> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../overview-summary.html"><FONT CLASS="NavBarFont1"><B>Overview</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="package-summary.html"><FONT CLASS="NavBarFont1"><B>Package</B></FONT></A> </TD> <TD BGCOLOR="#FFFFFF" CLASS="NavBarCell1Rev"> <FONT CLASS="NavBarFont1Rev"><B>Class</B></FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="package-tree.html"><FONT CLASS="NavBarFont1"><B>Tree</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../deprecated-list.html"><FONT CLASS="NavBarFont1"><B>Deprecated</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../index-all.html"><FONT CLASS="NavBarFont1"><B>Index</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../help-doc.html"><FONT CLASS="NavBarFont1"><B>Help</B></FONT></A> </TD> </TR></TABLE></TD><TD ALIGN="right" VALIGN="top" ROWSPAN=3><EM></EM></TD></TR><TR><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../weka/classifiers/AdaBoostM1.html"><B>PREV CLASS</B></A> <A HREF="../../weka/classifiers/AttributeSelectedClassifier.html"><B>NEXT CLASS</B></A></FONT></TD><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../index.html" TARGET="_top"><B>FRAMES</B></A> <A HREF="AdditiveRegression.html" TARGET="_top"><B>NO FRAMES</B></A></FONT></TD></TR><TR><TD VALIGN="top" CLASS="NavBarCell3"><FONT SIZE="-2"> SUMMARY: INNER | <A HREF="#field_summary">FIELD</A> | <A HREF="#constructor_summary">CONSTR</A> | <A HREF="#method_summary">METHOD</A></FONT></TD><TD VALIGN="top" CLASS="NavBarCell3"><FONT SIZE="-2">DETAIL: <A HREF="#field_detail">FIELD</A> | <A HREF="#constructor_detail">CONSTR</A> | <A HREF="#method_detail">METHOD</A></FONT></TD></TR></TABLE><!-- =========== END OF NAVBAR =========== --><HR><!-- ======== START OF CLASS DATA ======== --><H2><FONT SIZE="-1">weka.classifiers</FONT><BR>Class AdditiveRegression</H2><PRE>java.lang.Object | +--<A HREF="../../weka/classifiers/Classifier.html">weka.classifiers.Classifier</A> | +--<B>weka.classifiers.AdditiveRegression</B></PRE><DL><DT><B>All Implemented Interfaces:</B> <DD><A HREF="../../weka/core/AdditionalMeasureProducer.html">AdditionalMeasureProducer</A>, java.lang.Cloneable, <A HREF="../../weka/core/OptionHandler.html">OptionHandler</A>, java.io.Serializable</DD></DL><HR><DL><DT>public class <B>AdditiveRegression</B><DT>extends <A HREF="../../weka/classifiers/Classifier.html">Classifier</A><DT>implements <A HREF="../../weka/core/OptionHandler.html">OptionHandler</A>, <A HREF="../../weka/core/AdditionalMeasureProducer.html">AdditionalMeasureProducer</A></DL><P>Meta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left by the classifier on the previous iteration. Prediction is accomplished by adding the predictions of each classifier. Smoothing is accomplished through varying the shrinkage (learning rate) parameter. <p> <pre> Analysing: Root_relative_squared_error Datasets: 36 Resultsets: 2 Confidence: 0.05 (two tailed) Date: 10/13/00 10:00 AM Dataset (1) m5.M5Prim | (2) AdditiveRegression -S 0.7 \ | -B weka.classifiers.m5.M5Prime ---------------------------- auto93.names (10) 54.4 | 49.41 * autoHorse.names (10) 32.76 | 26.34 * autoMpg.names (10) 35.32 | 34.84 * autoPrice.names (10) 40.01 | 36.57 * baskball (10) 79.46 | 79.85 bodyfat.names (10) 10.38 | 11.41 v bolts (10) 19.29 | 12.61 * breastTumor (10) 96.95 | 96.23 * cholesterol (10) 101.03 | 98.88 * cleveland (10) 71.29 | 70.87 * cloud (10) 38.82 | 39.18 cpu (10) 22.26 | 14.74 * detroit (10) 228.16 | 83.7 * echoMonths (10) 71.52 | 69.15 * elusage (10) 48.94 | 49.03 fishcatch (10) 16.61 | 15.36 * fruitfly (10) 100 | 100 * gascons (10) 18.72 | 14.26 * housing (10) 38.62 | 36.53 * hungarian (10) 74.67 | 72.19 * longley (10) 31.23 | 28.26 * lowbwt (10) 62.26 | 61.48 * mbagrade (10) 89.2 | 89.2 meta (10) 163.15 | 188.28 v pbc (10) 81.35 | 79.4 * pharynx (10) 105.41 | 105.03 pollution (10) 72.24 | 68.16 * pwLinear (10) 32.42 | 33.33 v quake (10) 100.21 | 99.93 schlvote (10) 92.41 | 98.23 v sensory (10) 88.03 | 87.94 servo (10) 37.07 | 35.5 * sleep (10) 70.17 | 71.65 strike (10) 84.98 | 83.96 * veteran (10) 90.61 | 88.77 * vineyard (10) 79.41 | 73.95 * ---------------------------- (v| |*) | (4|8|24) </pre> <p> For more information see: <p> Friedman, J.H. (1999). Stochastic Gradient Boosting. Technical Report Stanford University. http://www-stat.stanford.edu/~jhf/ftp/stobst.ps. <p> Valid options from the command line are: <p> -B classifierstring <br> Classifierstring should contain the full class name of a classifier followed by options to the classifier. (required).<p> -S shrinkage rate <br> Smaller values help prevent overfitting and have a smoothing effect (but increase learning time). (default = 1.0, ie no shrinkage). <p> -M max models <br> Set the maximum number of models to generate. Values <= 0 indicate no maximum, ie keep going until the reduction in error threshold is reached. (default = -1). <p> -D <br> Debugging output. <p><P><DL><DT><B>See Also: </B><DD><A HREF="../../serialized-form.html#weka.classifiers.AdditiveRegression">Serialized Form</A></DL><HR><P><!-- ======== INNER CLASS SUMMARY ======== --><!-- =========== FIELD SUMMARY =========== --><A NAME="field_summary"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=2><FONT SIZE="+2"><B>Field Summary</B></FONT></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>protected <A HREF="../../weka/classifiers/Classifier.html">Classifier</A></CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#m_Classifier">m_Classifier</A></B></CODE><BR> Base classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>protected int</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#m_maxModels">m_maxModels</A></B></CODE><BR> Maximum number of models to produce.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>protected double</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#m_shrinkage">m_shrinkage</A></B></CODE><BR> Shrinkage (Learning rate).</TD></TR></TABLE> <!-- ======== CONSTRUCTOR SUMMARY ======== --><A NAME="constructor_summary"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=2><FONT SIZE="+2"><B>Constructor Summary</B></FONT></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#AdditiveRegression()">AdditiveRegression</A></B>()</CODE><BR> Default constructor specifying DecisionStump as the classifier</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#AdditiveRegression(weka.classifiers.Classifier)">AdditiveRegression</A></B>(<A HREF="../../weka/classifiers/Classifier.html">Classifier</A> classifier)</CODE><BR> Constructor which takes base classifier as argument.</TD></TR></TABLE> <!-- ========== METHOD SUMMARY =========== --><A NAME="method_summary"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=2><FONT SIZE="+2"><B>Method Summary</B></FONT></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/AdditiveRegression.html#buildClassifier(weka.core.Instances)">buildClassifier</A></B>(<A HREF="../../weka/core/Instances.html">Instances</A> data)</CODE><BR> Build the classifier on the supplied data</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/AdditiveRegression.html#classifierTipText()">classifierTipText</A></B>()</CODE><BR> Returns the tip text for this property</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/AdditiveRegression.html#classifyInstance(weka.core.Instance)">classifyInstance</A></B>(<A HREF="../../weka/core/Instance.html">Instance</A> inst)</CODE><BR> Classify an instance.</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/AdditiveRegression.html#debugTipText()">debugTipText</A></B>()</CODE><BR> Returns the tip text for this property</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.util.Enumeration</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#enumerateMeasures()">enumerateMeasures</A></B>()</CODE><BR> Returns an enumeration of the additional measure names</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> <A HREF="../../weka/classifiers/Classifier.html">Classifier</A></CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#getClassifier()">getClassifier</A></B>()</CODE><BR> Gets the classifier used.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>protected java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#getClassifierSpec()">getClassifierSpec</A></B>()</CODE><BR> Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> boolean</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#getDebug()">getDebug</A></B>()</CODE><BR> Gets whether debugging has been turned on</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> int</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/AdditiveRegression.html#getMaxModels()">getMaxModels</A></B>()</CODE><BR> Get the max number of models to generate</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/AdditiveRegression.html#getMeasure(java.lang.String)">getMeasure</A></B>(java.lang.String additionalMeasureName)</CODE><BR> Returns the value of the named measure</TD>⌨️ 快捷键说明
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