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<a href="pyspark-module.html">Package pyspark</a> ::
<a href="pyspark.mllib-module.html">Package mllib</a> ::
<a href="pyspark.mllib.classification-module.html">Module classification</a> ::
Class LogisticRegressionModel
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class LogisticRegressionModel</h1><p class="nomargin-top"><span class="codelink"><a href="pyspark.mllib.classification-pysrc.html#LogisticRegressionModel">source code</a></span></p>
<pre class="base-tree">
_common.LinearModel --+
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<strong class="uidshort">LogisticRegressionModel</strong>
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<hr />
<p>A linear binary classification model derived from logistic
regression.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
<span class="py-prompt">>>> </span>lrm = LogisticRegressionWithSGD.train(sc.parallelize(data))
<span class="py-prompt">>>> </span>lrm.predict(array([1.0])) > 0
<span class="py-output">True</span></pre>
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<td><span class="summary-sig"><a name="predict"></a><span class="summary-sig-name">predict</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">x</span>)</span></td>
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<span class="codelink"><a href="pyspark.mllib.classification-pysrc.html#LogisticRegressionModel.predict">source code</a></span>
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