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<TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/rules/SimpleRuleLearner.html" title="class in com.rapidminer.operator.learner.rules">SimpleRuleLearner</A></B></CODE><BR> This operator builds an unpruned rule set of classification rules.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/rules/SingleRuleLearner.html" title="class in com.rapidminer.operator.learner.rules">SingleRuleLearner</A></B></CODE><BR> This operator concentrates on one single attribute and determines the best splitting terms for minimizing the training error.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.tree"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2">Uses of <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/tree/package-summary.html">com.rapidminer.operator.learner.tree</A></FONT></TH></TR></TABLE> <P><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor"><TH ALIGN="left" COLSPAN="2">Classes in <A HREF="../../../../../com/rapidminer/operator/learner/tree/package-summary.html">com.rapidminer.operator.learner.tree</A> that implement <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/AbstractTreeLearner.html" title="class in com.rapidminer.operator.learner.tree">AbstractTreeLearner</A></B></CODE><BR> This is the abstract super class for all decision tree learners.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/CHAIDLearner.html" title="class in com.rapidminer.operator.learner.tree">CHAIDLearner</A></B></CODE><BR> The CHAID decision tree learner works like the <A HREF="../../../../../com/rapidminer/operator/learner/tree/DecisionTreeLearner.html" title="class in com.rapidminer.operator.learner.tree"><CODE>DecisionTreeLearner</CODE></A> with one exception: it used a chi squared based criterion instead of the information gain or gain ratio criteria.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/DecisionStumpLearner.html" title="class in com.rapidminer.operator.learner.tree">DecisionStumpLearner</A></B></CODE><BR> This operator learns decision stumps, i.e. a small decision tree with only one single split.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/DecisionTreeLearner.html" title="class in com.rapidminer.operator.learner.tree">DecisionTreeLearner</A></B></CODE><BR> This operator learns decision trees from both nominal and numerical data.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/ID3Learner.html" title="class in com.rapidminer.operator.learner.tree">ID3Learner</A></B></CODE><BR> This operator learns decision trees without pruning using nominal attributes only.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/ID3NumericalLearner.html" title="class in com.rapidminer.operator.learner.tree">ID3NumericalLearner</A></B></CODE><BR> This operator learns decision trees without pruning using both nominal and numerical attributes.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/MultiCriterionDecisionStumps.html" title="class in com.rapidminer.operator.learner.tree">MultiCriterionDecisionStumps</A></B></CODE><BR> A DecisionStump clone that allows to specify different utility functions.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/RandomForestLearner.html" title="class in com.rapidminer.operator.learner.tree">RandomForestLearner</A></B></CODE><BR> This operators learns a random forest.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/RandomTreeLearner.html" title="class in com.rapidminer.operator.learner.tree">RandomTreeLearner</A></B></CODE><BR> This operator learns decision trees from both nominal and numerical data.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/RelevanceTreeLearner.html" title="class in com.rapidminer.operator.learner.tree">RelevanceTreeLearner</A></B></CODE><BR> Learns a pruned decision tree based on arbitrary feature relevance measurements defined by an inner operator (use for example <A HREF="../../../../../com/rapidminer/operator/features/weighting/InfoGainRatioWeighting.html" title="class in com.rapidminer.operator.features.weighting"><CODE>InfoGainRatioWeighting</CODE></A> for C4.5 and <A HREF="../../../../../com/rapidminer/operator/features/weighting/ChiSquaredWeighting.html" title="class in com.rapidminer.operator.features.weighting"><CODE>ChiSquaredWeighting</CODE></A> for CHAID.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.weka"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2">Uses of <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/weka/package-summary.html">com.rapidminer.operator.learner.weka</A></FONT></TH></TR></TABLE> <P><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor"><TH ALIGN="left" COLSPAN="2">Classes in <A HREF="../../../../../com/rapidminer/operator/learner/weka/package-summary.html">com.rapidminer.operator.learner.weka</A> that implement <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/weka/GenericWekaEnsembleLearner.html" title="class in com.rapidminer.operator.learner.weka">GenericWekaEnsembleLearner</A></B></CODE><BR> Performs the ensemble learning scheme of Weka with the same name.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/weka/GenericWekaLearner.html" title="class in com.rapidminer.operator.learner.weka">GenericWekaLearner</A></B></CODE><BR> Performs the Weka learning scheme with the same name.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/weka/GenericWekaMetaLearner.html" title="class in com.rapidminer.operator.learner.weka">GenericWekaMetaLearner</A></B></CODE><BR> Performs the meta learning scheme of Weka with the same name.</TD></TR></TABLE> <P><HR><!-- ======= START OF BOTTOM NAVBAR ====== --><A NAME="navbar_bottom"><!-- --></A><A HREF="#skip-navbar_bottom" title="Skip 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