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<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/FrequencyCalculator.html" title="class in com.rapidminer.operator.learner.tree">FrequencyCalculator</A></B></TD>
<TD>Calculates frequencies and weights.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/GainRatioCriterion.html" title="class in com.rapidminer.operator.learner.tree">GainRatioCriterion</A></B></TD>
<TD>The gain ratio divides the information gain by the prior split
 info in order to prevent id-like attributes to be selected as the best.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/GiniIndexCriterion.html" title="class in com.rapidminer.operator.learner.tree">GiniIndexCriterion</A></B></TD>
<TD>Calculates the Gini index for the given split.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/GreaterSplitCondition.html" title="class in com.rapidminer.operator.learner.tree">GreaterSplitCondition</A></B></TD>
<TD>Returns true if the value of the desired attribute is greater then a given threshold.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/ID3Learner.html" title="class in com.rapidminer.operator.learner.tree">ID3Learner</A></B></TD>
<TD>This operator learns decision trees without pruning using nominal
 attributes only.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/ID3NumericalLearner.html" title="class in com.rapidminer.operator.learner.tree">ID3NumericalLearner</A></B></TD>
<TD>This operator learns decision trees without pruning using both nominal
 and numerical attributes.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/InfoGainCriterion.html" title="class in com.rapidminer.operator.learner.tree">InfoGainCriterion</A></B></TD>
<TD>This criterion implements the well known information gain in 
 order to calculate the benefit of a split.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/LessEqualsSplitCondition.html" title="class in com.rapidminer.operator.learner.tree">LessEqualsSplitCondition</A></B></TD>
<TD>A split condition for numerical values (less equals).</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/MaxDepthTermination.html" title="class in com.rapidminer.operator.learner.tree">MaxDepthTermination</A></B></TD>
<TD>Terminates if a maximal depth is reached.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/MinSizeTermination.html" title="class in com.rapidminer.operator.learner.tree">MinSizeTermination</A></B></TD>
<TD>Terminates if the example set has less than minSize examples.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/MultiCriterionDecisionStumps.html" title="class in com.rapidminer.operator.learner.tree">MultiCriterionDecisionStumps</A></B></TD>
<TD>A DecisionStump clone that allows to specify different utility functions.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/MultiCriterionDecisionStumps.DecisionStumpModel.html" title="class in com.rapidminer.operator.learner.tree">MultiCriterionDecisionStumps.DecisionStumpModel</A></B></TD>
<TD>&nbsp;</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/NoAttributeLeftTermination.html" title="class in com.rapidminer.operator.learner.tree">NoAttributeLeftTermination</A></B></TD>
<TD>Terminates if the example set does not have any regular attributes.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/NominalSplitCondition.html" title="class in com.rapidminer.operator.learner.tree">NominalSplitCondition</A></B></TD>
<TD>A split condition for nominal values (equals).</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/NumericalSplitter.html" title="class in com.rapidminer.operator.learner.tree">NumericalSplitter</A></B></TD>
<TD>Calculates the best split point for numerical attributes according to 
 a given criterion.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/PessimisticPruner.html" title="class in com.rapidminer.operator.learner.tree">PessimisticPruner</A></B></TD>
<TD>This class provides a pruner based on some heuristic statistics.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/RandomForestLearner.html" title="class in com.rapidminer.operator.learner.tree">RandomForestLearner</A></B></TD>
<TD>This operators learns a random forest.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/RandomSubsetPreprocessing.html" title="class in com.rapidminer.operator.learner.tree">RandomSubsetPreprocessing</A></B></TD>
<TD>Selects a random subset.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/RandomTreeLearner.html" title="class in com.rapidminer.operator.learner.tree">RandomTreeLearner</A></B></TD>
<TD>
 This operator learns decision trees from both nominal and numerical data.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/RelevanceTreeLearner.html" title="class in com.rapidminer.operator.learner.tree">RelevanceTreeLearner</A></B></TD>
<TD>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>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/SingleLabelTermination.html" title="class in com.rapidminer.operator.learner.tree">SingleLabelTermination</A></B></TD>
<TD>This criterion terminates if only one single label is left.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/Tree.html" title="class in com.rapidminer.operator.learner.tree">Tree</A></B></TD>
<TD>A tree is a node in a tree model containing several edges to other
 trees (children) combined with conditions at these edges.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/TreeBuilder.html" title="class in com.rapidminer.operator.learner.tree">TreeBuilder</A></B></TD>
<TD>Build a tree from an example set.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD WIDTH="15%"><B><A HREF="../../../../../com/rapidminer/operator/learner/tree/TreeModel.html" title="class in com.rapidminer.operator.learner.tree">TreeModel</A></B></TD>
<TD>The tree model is the model created by all decision trees.</TD>
</TR>
</TABLE>
&nbsp;

<P>
<A NAME="package_description"><!-- --></A><H2>
Package com.rapidminer.operator.learner.tree Description
</H2>

<P>
Provides decision tree learners.
<P>

<P>
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