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<h1 class="title"><a href="mllib-guide.html">MLlib</a> - Classification and Regression</h1>
<p>MLlib supports various methods for
<a href="http://en.wikipedia.org/wiki/Binary_classification">binary classification</a>,
<a href="http://en.wikipedia.org/wiki/Multiclass_classification">multiclass
classification</a>, and
<a href="http://en.wikipedia.org/wiki/Regression_analysis">regression analysis</a>. The table below outlines
the supported algorithms for each type of problem.</p>
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<tr><th>Problem Type</th><th>Supported Methods</th></tr>
</thead>
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<tr>
<td>Binary Classification</td><td>linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes</td>
</tr>
<tr>
<td>Multiclass Classification</td><td>decision trees, random forests, naive Bayes</td>
</tr>
<tr>
<td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression</td>
</tr>
</tbody>
</table>
<p>More details for these methods can be found here:</p>
<ul>
<li><a href="mllib-linear-methods.html">Linear models</a>
<ul>
<li><a href="mllib-linear-methods.html#binary-classification">binary classification (SVMs, logistic regression)</a></li>
<li><a href="mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression">linear regression (least squares, Lasso, ridge)</a></li>
</ul>
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<li><a href="mllib-decision-tree.html">Decision trees</a></li>
<li><a href="mllib-ensembles.html">Ensembles of decision trees</a>
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<li><a href="mllib-ensembles.html#random-forests">random forests</a></li>
<li><a href="mllib-ensembles.html#gradient-boosted-trees-gbts">gradient-boosted trees</a></li>
</ul>
</li>
<li><a href="mllib-naive-bayes.html">Naive Bayes</a></li>
<li><a href="mllib-isotonic-regression.html">Isotonic regression</a></li>
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