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<div class="subTitle">org.apache.spark.ml.classification</div>
<h2 title="Class BinaryLogisticRegressionSummary" class="title">Class BinaryLogisticRegressionSummary</h2>
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<div class="contentContainer">
<ul class="inheritance">
<li>java.lang.Object</li>
<li>
<ul class="inheritance">
<li>org.apache.spark.ml.classification.BinaryLogisticRegressionSummary</li>
</ul>
</li>
</ul>
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<dl>
<dt>All Implemented Interfaces:</dt>
<dd>java.io.Serializable, <a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html" title="interface in org.apache.spark.ml.classification">LogisticRegressionSummary</a></dd>
</dl>
<dl>
<dt>Direct Known Subclasses:</dt>
<dd><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionTrainingSummary.html" title="class in org.apache.spark.ml.classification">BinaryLogisticRegressionTrainingSummary</a></dd>
</dl>
<hr>
<br>
<pre>public class <span class="strong">BinaryLogisticRegressionSummary</span>
extends java.lang.Object
implements <a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html" title="interface in org.apache.spark.ml.classification">LogisticRegressionSummary</a></pre>
<div class="block">:: Experimental ::
Binary Logistic regression results for a given model.
param: predictions dataframe outputted by the model's <code>transform</code> method.
param: probabilityCol field in "predictions" which gives the calibrated probability of
each sample.
param: labelCol field in "predictions" which gives the true label of each sample.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.ml.classification.BinaryLogisticRegressionSummary">Serialized Form</a></dd></dl>
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<h3>Method Summary</h3>
<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
<caption><span>Methods</span><span class="tabEnd"> </span></caption>
<tr>
<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
</tr>
<tr class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#areaUnderROC()">areaUnderROC</a></strong>()</code>
<div class="block">Computes the area under the receiver operating characteristic (ROC) curve.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#fMeasureByThreshold()">fMeasureByThreshold</a></strong>()</code>
<div class="block">Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>java.lang.String</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#labelCol()">labelCol</a></strong>()</code>
<div class="block">Field in "predictions" which gives the the true label of each sample.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#pr()">pr</a></strong>()</code>
<div class="block">Returns the precision-recall curve, which is an Dataframe containing
two fields recall, precision with (0.0, 1.0) prepended to it.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#precisionByThreshold()">precisionByThreshold</a></strong>()</code>
<div class="block">Returns a dataframe with two fields (threshold, precision) curve.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#predictions()">predictions</a></strong>()</code>
<div class="block">Dataframe outputted by the model's `transform` method.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>java.lang.String</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#probabilityCol()">probabilityCol</a></strong>()</code>
<div class="block">Field in "predictions" which gives the calibrated probability of each sample as a vector.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#recallByThreshold()">recallByThreshold</a></strong>()</code>
<div class="block">Returns a dataframe with two fields (threshold, recall) curve.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#roc()">roc</a></strong>()</code>
<div class="block">Returns the receiver operating characteristic (ROC) curve,
which is an Dataframe having two fields (FPR, TPR)
with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.</div>
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<h3>Methods inherited from class java.lang.Object</h3>
<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
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<h3>Method Detail</h3>
<a name="predictions()">
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<h4>predictions</h4>
<pre>public <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> predictions()</pre>
<div class="block"><strong>Description copied from interface: <code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#predictions()">LogisticRegressionSummary</a></code></strong></div>
<div class="block">Dataframe outputted by the model's `transform` method.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#predictions()">predictions</a></code> in interface <code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html" title="interface in org.apache.spark.ml.classification">LogisticRegressionSummary</a></code></dd>
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<a name="probabilityCol()">
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<h4>probabilityCol</h4>
<pre>public java.lang.String probabilityCol()</pre>
<div class="block"><strong>Description copied from interface: <code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#probabilityCol()">LogisticRegressionSummary</a></code></strong></div>
<div class="block">Field in "predictions" which gives the calibrated probability of each sample as a vector.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#probabilityCol()">probabilityCol</a></code> in interface <code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html" title="interface in org.apache.spark.ml.classification">LogisticRegressionSummary</a></code></dd>
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<h4>labelCol</h4>
<pre>public java.lang.String labelCol()</pre>
<div class="block"><strong>Description copied from interface: <code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#labelCol()">LogisticRegressionSummary</a></code></strong></div>
<div class="block">Field in "predictions" which gives the the true label of each sample.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#labelCol()">labelCol</a></code> in interface <code><a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html" title="interface in org.apache.spark.ml.classification">LogisticRegressionSummary</a></code></dd>
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<h4>roc</h4>
<pre>public <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> roc()</pre>
<div class="block">Returns the receiver operating characteristic (ROC) curve,
which is an Dataframe having two fields (FPR, TPR)
with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd><dt><span class="strong">See Also:</span></dt><dd><code>http://en.wikipedia.org/wiki/Receiver_operating_characteristic</code></dd></dl>
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<h4>areaUnderROC</h4>
<pre>public double areaUnderROC()</pre>
<div class="block">Computes the area under the receiver operating characteristic (ROC) curve.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>pr</h4>
<pre>public <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> pr()</pre>
<div class="block">Returns the precision-recall curve, which is an Dataframe containing
two fields recall, precision with (0.0, 1.0) prepended to it.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
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<a name="fMeasureByThreshold()">
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<h4>fMeasureByThreshold</h4>
<pre>public <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> fMeasureByThreshold()</pre>
<div class="block">Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>precisionByThreshold</h4>
<pre>public <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> precisionByThreshold()</pre>
<div class="block">Returns a dataframe with two fields (threshold, precision) curve.
Every possible probability obtained in transforming the dataset are used
as thresholds used in calculating the precision.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<a name="recallByThreshold()">
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<h4>recallByThreshold</h4>
<pre>public <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> recallByThreshold()</pre>
<div class="block">Returns a dataframe with two fields (threshold, recall) curve.
Every possible probability obtained in transforming the dataset are used
as thresholds used in calculating the recall.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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