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<div class="subTitle">org.apache.spark.mllib.tree.loss</div>
<h2 title="Class SquaredError" class="title">Class SquaredError</h2>
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<li>java.lang.Object</li>
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<dd>java.io.Serializable, <a href="../../../../../../org/apache/spark/mllib/tree/loss/Loss.html" title="interface in org.apache.spark.mllib.tree.loss">Loss</a></dd>
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<pre>public class <span class="strong">SquaredError</span>
extends java.lang.Object
implements <a href="../../../../../../org/apache/spark/mllib/tree/loss/Loss.html" title="interface in org.apache.spark.mllib.tree.loss">Loss</a></pre>
<div class="block">:: DeveloperApi ::
Class for squared error loss calculation.
<p>
The squared (L2) error is defined as:
(y - F(x))**2
where y is the label and F(x) is the model prediction for features x.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../../serialized-form.html#org.apache.spark.mllib.tree.loss.SquaredError">Serialized Form</a></dd></dl>
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<td class="colOne"><code><strong><a href="../../../../../../org/apache/spark/mllib/tree/loss/SquaredError.html#SquaredError()">SquaredError</a></strong>()</code> </td>
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<td class="colFirst"><code>static double</code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/tree/loss/SquaredError.html#gradient(double, double)">gradient</a></strong>(double prediction,
double label)</code>
<div class="block">Method to calculate the gradients for the gradient boosting calculation for least
squares error calculation.</div>
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<h3>Methods inherited from interface org.apache.spark.mllib.tree.loss.<a href="../../../../../../org/apache/spark/mllib/tree/loss/Loss.html" title="interface in org.apache.spark.mllib.tree.loss">Loss</a></h3>
<code><a href="../../../../../../org/apache/spark/mllib/tree/loss/Loss.html#computeError(double, double)">computeError</a>, <a href="../../../../../../org/apache/spark/mllib/tree/loss/Loss.html#computeError(org.apache.spark.mllib.tree.model.TreeEnsembleModel, org.apache.spark.rdd.RDD)">computeError</a>, <a href="../../../../../../org/apache/spark/mllib/tree/loss/Loss.html#gradient(double, double)">gradient</a></code></li>
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<h4>SquaredError</h4>
<pre>public SquaredError()</pre>
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<h4>gradient</h4>
<pre>public static double gradient(double prediction,
double label)</pre>
<div class="block">Method to calculate the gradients for the gradient boosting calculation for least
squares error calculation.
The gradient with respect to F(x) is: - 2 (y - F(x))</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>prediction</code> - Predicted label.</dd><dd><code>label</code> - True label.</dd>
<dt><span class="strong">Returns:</span></dt><dd>Loss gradient</dd></dl>
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