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| <div class="section" id="naivebayes"> |
| <h1>NaiveBayes<a class="headerlink" href="#naivebayes" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.classification.NaiveBayes"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.classification.</code><code class="sig-name descname">NaiveBayes</code><a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#NaiveBayes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.NaiveBayes" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Train a Multinomial Naive Bayes model.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| <p class="rubric">Methods</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.NaiveBayes.train" title="pyspark.mllib.classification.NaiveBayes.train"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code></a>(data[, lambda_])</p></td> |
| <td><p>Train a Naive Bayes model given an RDD of (label, features) vectors.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.NaiveBayes.train"> |
| <em class="property">classmethod </em><code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span><span class="p">:</span> <span class="n">pyspark.rdd.RDD<span class="p">[</span><a class="reference internal" href="pyspark.mllib.regression.LabeledPoint.html#pyspark.mllib.regression.LabeledPoint" title="pyspark.mllib.regression.LabeledPoint">pyspark.mllib.regression.LabeledPoint</a><span class="p">]</span></span></em>, <em class="sig-param"><span class="n">lambda_</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1.0</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="pyspark.mllib.classification.NaiveBayesModel.html#pyspark.mllib.classification.NaiveBayesModel" title="pyspark.mllib.classification.NaiveBayesModel">pyspark.mllib.classification.NaiveBayesModel</a><a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#NaiveBayes.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.NaiveBayes.train" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Train a Naive Bayes model given an RDD of (label, features) |
| vectors.</p> |
| <p>This is the <a class="reference external" href="http://tinyurl.com/lsdw6p">Multinomial NB</a> which |
| can handle all kinds of discrete data. For example, by |
| converting documents into TF-IDF vectors, it can be used for |
| document classification. By making every vector a 0-1 vector, |
| it can also be used as <a class="reference external" href="http://tinyurl.com/p7c96j6">Bernoulli NB</a>. |
| The input feature values must be nonnegative.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>data</strong><span class="classifier"><a class="reference internal" href="pyspark.RDD.html#pyspark.RDD" title="pyspark.RDD"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD</span></code></a></span></dt><dd><p>The training data, an RDD of <a class="reference internal" href="pyspark.mllib.regression.LabeledPoint.html#pyspark.mllib.regression.LabeledPoint" title="pyspark.mllib.regression.LabeledPoint"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.mllib.regression.LabeledPoint</span></code></a>.</p> |
| </dd> |
| <dt><strong>lambda_</strong><span class="classifier">float, optional</span></dt><dd><p>The smoothing parameter. |
| (default: 1.0)</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
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