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<title>MADlib: Tree Methods</title>
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<a href="#groups">Modules</a>  </div>
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<div class="title">Tree Methods<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a></div></div>  </div>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<p>A collection of recursive partitioning (tree) methods. </p>
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Modules</h2></td></tr>
<tr class="memitem:group__grp__decision__tree"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__decision__tree.html">Decision Tree</a></td></tr>
<tr class="memdesc:group__grp__decision__tree"><td class="mdescLeft">&#160;</td><td class="mdescRight">Decision Trees. Decision trees use a tree-based model to predict the value of a target variable based on several input variables. <br /></td></tr>
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<tr class="memitem:group__grp__random__forest"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__random__forest.html">Random Forest</a></td></tr>
<tr class="memdesc:group__grp__random__forest"><td class="mdescLeft">&#160;</td><td class="mdescRight">Random forests are an ensemble learning method for classification and regression that construct a multitude of decision trees at training time, then produce the class that is the mode of the classes of the individual trees. <br /></td></tr>
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