package-summary.html
来自「数据挖掘方面最新软件」· HTML 代码 · 共 344 行 · 第 1/2 页
HTML
344 行
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"><!--NewPage--><HTML><HEAD><!-- Generated by javadoc (build 1.5.0_13) on Mon Jul 14 01:36:54 CEST 2008 --><TITLE>com.rapidminer.operator.learner.meta (RapidMiner Class Documentation)</TITLE><META NAME="keywords" CONTENT="com.rapidminer.operator.learner.meta package"><LINK REL ="stylesheet" TYPE="text/css" HREF="../../../../../stylesheet.css" TITLE="Style"><SCRIPT type="text/javascript">function windowTitle(){ parent.document.title="com.rapidminer.operator.learner.meta (RapidMiner Class Documentation)";}</SCRIPT><NOSCRIPT></NOSCRIPT></HEAD><BODY BGCOLOR="white" onload="windowTitle();"><!-- ========= START OF TOP NAVBAR ======= --><A NAME="navbar_top"><!-- --></A><A HREF="#skip-navbar_top" title="Skip navigation links"></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0" SUMMARY=""><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_top_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3" SUMMARY=""> <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="#FFFFFF" CLASS="NavBarCell1Rev"> <FONT CLASS="NavBarFont1Rev"><B>Package</B></FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <FONT CLASS="NavBarFont1">Class</FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="package-use.html"><FONT CLASS="NavBarFont1"><B>Use</B></FONT></A> </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="../../../../../com/rapidminer/operator/learner/lazy/package-summary.html"><B>PREV PACKAGE</B></A> <A HREF="../../../../../com/rapidminer/operator/learner/rules/package-summary.html"><B>NEXT PACKAGE</B></A></FONT></TD><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../../../../index.html?com/rapidminer/operator/learner/meta/package-summary.html" target="_top"><B>FRAMES</B></A> <A HREF="package-summary.html" target="_top"><B>NO FRAMES</B></A> <SCRIPT type="text/javascript"> <!-- if(window==top) { document.writeln('<A HREF="../../../../../allclasses-noframe.html"><B>All Classes</B></A>'); } //--></SCRIPT><NOSCRIPT> <A HREF="../../../../../allclasses-noframe.html"><B>All Classes</B></A></NOSCRIPT></FONT></TD></TR></TABLE><A NAME="skip-navbar_top"></A><!-- ========= END OF TOP NAVBAR ========= --><HR><H2>Package com.rapidminer.operator.learner.meta</H2>Meta learning schemes which uses other learning operators to increase the performance.<P><B>See:</B><BR> <A HREF="#package_description"><B>Description</B></A><P><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2"><B>Class Summary</B></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AbstractMetaLearner.html" title="class in com.rapidminer.operator.learner.meta">AbstractMetaLearner</A></B></TD><TD>A <tt>MetaLearner</tt> is an operator that encapsulates one or more learning steps to build its model.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AbstractStacking.html" title="class in com.rapidminer.operator.learner.meta">AbstractStacking</A></B></TD><TD>This class uses n+1 inner learners and generates n different models by using the last n learners.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AdaBoost.html" title="class in com.rapidminer.operator.learner.meta">AdaBoost</A></B></TD><TD>This AdaBoost implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AdaBoostModel.html" title="class in com.rapidminer.operator.learner.meta">AdaBoostModel</A></B></TD><TD>A model for the RapidMiner AdaBoost implementation.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AdaBoostPerformanceMeasures.html" title="class in com.rapidminer.operator.learner.meta">AdaBoostPerformanceMeasures</A></B></TD><TD>Helper class for the internal AdaBoost implementation.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AdditiveRegression.html" title="class in com.rapidminer.operator.learner.meta">AdditiveRegression</A></B></TD><TD>This operator uses regression learner as a base learner.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AdditiveRegressionModel.html" title="class in com.rapidminer.operator.learner.meta">AdditiveRegressionModel</A></B></TD><TD>The model created by an AdditiveRegression meta learner.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Bagging.html" title="class in com.rapidminer.operator.learner.meta">Bagging</A></B></TD><TD>This Bagging implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BaggingModel.html" title="class in com.rapidminer.operator.learner.meta">BaggingModel</A></B></TD><TD>The model for the internal Bagging implementation.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BayBoostBaseModelInfo.html" title="class in com.rapidminer.operator.learner.meta">BayBoostBaseModelInfo</A></B></TD><TD>Stores a base model together with its contingency matrix, which offerers a more convenient access in the context of ensemble classification.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BayBoostModel.html" title="class in com.rapidminer.operator.learner.meta">BayBoostModel</A></B></TD><TD>A model for the Bayesian Boosting algorithm by Martin Scholz.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BayBoostStream.html" title="class in com.rapidminer.operator.learner.meta">BayBoostStream</A></B></TD><TD>Assumptions: target label is always boolean goal is to fit a crisp ensemble classifier (use_distribution always off) base classifier weights are always adapted by a single row from first to last no internal bootstrapping </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BayBoostStream.BatchFilterCondition.html" title="class in com.rapidminer.operator.learner.meta">BayBoostStream.BatchFilterCondition</A></B></TD><TD>Class that filters an ExampleSet by the value of a special attribute.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BayesianBoosting.html" title="class in com.rapidminer.operator.learner.meta">BayesianBoosting</A></B></TD><TD>This operator trains an ensemble of classifiers for boolean target attributes.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Binary2MultiClassLearner.html" title="class in com.rapidminer.operator.learner.meta">Binary2MultiClassLearner</A></B></TD><TD>A metaclassifier for handling multi-class datasets with 2-class classifiers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Binary2MultiClassModel.html" title="class in com.rapidminer.operator.learner.meta">Binary2MultiClassModel</A></B></TD><TD>This operator uses an inner learning scheme which is able to perform predictions for binary or binominal classification problems and learns a set of these binary models in order to use this set for a given data set with more than two classes.</TD>⌨️ 快捷键说明
复制代码Ctrl + C
搜索代码Ctrl + F
全屏模式F11
增大字号Ctrl + =
减小字号Ctrl + -
显示快捷键?