<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<!-- NewPage -->
<html lang="en">
<head>
<!-- Generated by javadoc (version 1.7.0_51) on Wed Sep 16 10:26:35 PDT 2015 -->
<title>NaiveBayes</title>
<meta name="date" content="2015-09-16">
<link rel="stylesheet" type="text/css" href="../../../../../stylesheet.css" title="Style">
</head>
<body>
<script type="text/javascript"><!--
if (location.href.indexOf('is-external=true') == -1) {
parent.document.title="NaiveBayes";
}
//-->
</script>
<noscript>
<div>JavaScript is disabled on your browser.</div>
</noscript>
<!-- ========= START OF TOP NAVBAR ======= -->
<div class="topNav"><a name="navbar_top">
<!-- -->
</a><a href="#skip-navbar_top" title="Skip navigation links"></a><a name="navbar_top_firstrow">
<!-- -->
</a>
<ul class="navList" title="Navigation">
<li><a href="../../../../../overview-summary.html">Overview</a></li>
<li><a href="package-summary.html">Package</a></li>
<li class="navBarCell1Rev">Class</li>
<li><a href="package-tree.html">Tree</a></li>
<li><a href="../../../../../deprecated-list.html">Deprecated</a></li>
<li><a href="../../../../../index-all.html">Index</a></li>
<li><a href="../../../../../help-doc.html">Help</a></li>
</ul>
</div>
<div class="subNav">
<ul class="navList">
<li><a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification"><span class="strong">Prev Class</span></a></li>
<li><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification"><span class="strong">Next Class</span></a></li>
</ul>
<ul class="navList">
<li><a href="../../../../../index.html?org/apache/spark/mllib/classification/NaiveBayes.html" target="_top">Frames</a></li>
<li><a href="NaiveBayes.html" target="_top">No Frames</a></li>
</ul>
<ul class="navList" id="allclasses_navbar_top">
<li><a href="../../../../../allclasses-noframe.html">All Classes</a></li>
</ul>
<div>
<script type="text/javascript"><!--
allClassesLink = document.getElementById("allclasses_navbar_top");
if(window==top) {
allClassesLink.style.display = "block";
}
else {
allClassesLink.style.display = "none";
}
//-->
</script>
</div>
<div>
<ul class="subNavList">
<li>Summary: </li>
<li>Nested | </li>
<li>Field | </li>
<li><a href="#constructor_summary">Constr</a> | </li>
<li><a href="#method_summary">Method</a></li>
</ul>
<ul class="subNavList">
<li>Detail: </li>
<li>Field | </li>
<li><a href="#constructor_detail">Constr</a> | </li>
<li><a href="#method_detail">Method</a></li>
</ul>
</div>
<a name="skip-navbar_top">
<!-- -->
</a></div>
<!-- ========= END OF TOP NAVBAR ========= -->
<!-- ======== START OF CLASS DATA ======== -->
<div class="header">
<div class="subTitle">org.apache.spark.mllib.classification</div>
<h2 title="Class NaiveBayes" class="title">Class NaiveBayes</h2>
</div>
<div class="contentContainer">
<ul class="inheritance">
<li>java.lang.Object</li>
<li>
<ul class="inheritance">
<li>org.apache.spark.mllib.classification.NaiveBayes</li>
</ul>
</li>
</ul>
<div class="description">
<ul class="blockList">
<li class="blockList">
<dl>
<dt>All Implemented Interfaces:</dt>
<dd>java.io.Serializable, <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></dd>
</dl>
<hr>
<br>
<pre>public class <span class="strong">NaiveBayes</span>
extends java.lang.Object
implements scala.Serializable, <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></pre>
<div class="block">Trains a Naive Bayes model given an RDD of <code>(label, features)</code> pairs.
<p>
This is the Multinomial NB (<code>http://tinyurl.com/lsdw6p</code>) 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
Bernoulli NB (<code>http://tinyurl.com/p7c96j6</code>). The input feature values must be nonnegative.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.mllib.classification.NaiveBayes">Serialized Form</a></dd></dl>
</li>
</ul>
</div>
<div class="summary">
<ul class="blockList">
<li class="blockList">
<!-- ======== CONSTRUCTOR SUMMARY ======== -->
<ul class="blockList">
<li class="blockList"><a name="constructor_summary">
<!-- -->
</a>
<h3>Constructor Summary</h3>
<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Constructor Summary table, listing constructors, and an explanation">
<caption><span>Constructors</span><span class="tabEnd"> </span></caption>
<tr>
<th class="colOne" scope="col">Constructor and Description</th>
</tr>
<tr class="altColor">
<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#NaiveBayes()">NaiveBayes</a></strong>()</code> </td>
</tr>
<tr class="rowColor">
<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#NaiveBayes(double)">NaiveBayes</a></strong>(double lambda)</code> </td>
</tr>
</table>
</li>
</ul>
<!-- ========== METHOD SUMMARY =========== -->
<ul class="blockList">
<li class="blockList"><a name="method_summary">
<!-- -->
</a>
<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>static java.lang.String</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#Bernoulli()">Bernoulli</a></strong>()</code>
<div class="block">String name for Bernoulli model type.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#getLambda()">getLambda</a></strong>()</code>
<div class="block">Get the smoothing parameter.</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/mllib/classification/NaiveBayes.html#getModelType()">getModelType</a></strong>()</code>
<div class="block">Get the model type.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static java.lang.String</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#Multinomial()">Multinomial</a></strong>()</code>
<div class="block">String name for multinomial model type.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#run(org.apache.spark.rdd.RDD)">run</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> data)</code>
<div class="block">Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html" title="class in org.apache.spark.mllib.classification">NaiveBayes</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#setLambda(double)">setLambda</a></strong>(double lambda)</code>
<div class="block">Set the smoothing parameter.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html" title="class in org.apache.spark.mllib.classification">NaiveBayes</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#setModelType(java.lang.String)">setModelType</a></strong>(java.lang.String modelType)</code>
<div class="block">Set the model type using a string (case-sensitive).</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static scala.collection.immutable.Set<java.lang.String></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#supportedModelTypes()">supportedModelTypes</a></strong>()</code> </td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#train(org.apache.spark.rdd.RDD)">train</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> input)</code>
<div class="block">Trains a Naive Bayes model given an RDD of <code>(label, features)</code> pairs.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#train(org.apache.spark.rdd.RDD, double)">train</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> input,
double lambda)</code>
<div class="block">Trains a Naive Bayes model given an RDD of <code>(label, features)</code> pairs.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html#train(org.apache.spark.rdd.RDD, double, java.lang.String)">train</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> input,
double lambda,
java.lang.String modelType)</code>
<div class="block">Trains a Naive Bayes model given an RDD of <code>(label, features)</code> pairs.</div>
</td>
</tr>
</table>
<ul class="blockList">
<li class="blockList"><a name="methods_inherited_from_class_java.lang.Object">
<!-- -->
</a>
<h3>Methods inherited from class java.lang.Object</h3>
<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
</ul>
<ul class="blockList">
<li class="blockList"><a name="methods_inherited_from_class_org.apache.spark.Logging">
<!-- -->
</a>
<h3>Methods inherited from interface org.apache.spark.<a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></h3>
<code><a href="../../../../../org/apache/spark/Logging.html#initializeIfNecessary()">initializeIfNecessary</a>, <a href="../../../../../org/apache/spark/Logging.html#initializeLogging()">initializeLogging</a>, <a href="../../../../../org/apache/spark/Logging.html#isTraceEnabled()">isTraceEnabled</a>, <a href="../../../../../org/apache/spark/Logging.html#log_()">log_</a>, <a href="../../../../../org/apache/spark/Logging.html#log()">log</a>, <a href="../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0)">logDebug</a>, <a href="../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0, java.lang.Throwable)">logDebug</a>, <a href="../../../../../org/apache/spark/Logging.html#logError(scala.Function0)">logError</a>, <a href="../../../../../org/apache/spark/Logging.html#logError(scala.Function0, java.lang.Throwable)">logError</a>, <a href="../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0)">logInfo</a>, <a href="../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0, java.lang.Throwable)">logInfo</a>, <a href="../../../../../org/apache/spark/Logging.html#logName()">logName</a>, <a href="../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0)">logTrace</a>, <a href="../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0, java.lang.Throwable)">logTrace</a>, <a href="../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0)">logWarning</a>, <a href="../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0, java.lang.Throwable)">logWarning</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
<div class="details">
<ul class="blockList">
<li class="blockList">
<!-- ========= CONSTRUCTOR DETAIL ======== -->
<ul class="blockList">
<li class="blockList"><a name="constructor_detail">
<!-- -->
</a>
<h3>Constructor Detail</h3>
<a name="NaiveBayes(double)">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>NaiveBayes</h4>
<pre>public NaiveBayes(double lambda)</pre>
</li>
</ul>
<a name="NaiveBayes()">
<!-- -->
</a>
<ul class="blockListLast">
<li class="blockList">
<h4>NaiveBayes</h4>
<pre>public NaiveBayes()</pre>
</li>
</ul>
</li>
</ul>
<!-- ============ METHOD DETAIL ========== -->
<ul class="blockList">
<li class="blockList"><a name="method_detail">
<!-- -->
</a>
<h3>Method Detail</h3>
<a name="Multinomial()">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>Multinomial</h4>
<pre>public static java.lang.String Multinomial()</pre>
<div class="block">String name for multinomial model type.</div>
</li>
</ul>
<a name="Bernoulli()">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>Bernoulli</h4>
<pre>public static java.lang.String Bernoulli()</pre>
<div class="block">String name for Bernoulli model type.</div>
</li>
</ul>
<a name="supportedModelTypes()">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>supportedModelTypes</h4>
<pre>public static scala.collection.immutable.Set<java.lang.String> supportedModelTypes()</pre>
</li>
</ul>
<a name="train(org.apache.spark.rdd.RDD)">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>train</h4>
<pre>public static <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a> train(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> input)</pre>
<div class="block">Trains a Naive Bayes model given an RDD of <code>(label, features)</code> pairs.
<p>
This is the default Multinomial NB (<code>http://tinyurl.com/lsdw6p</code>) which can handle all
kinds of discrete data. For example, by converting documents into TF-IDF vectors, it
can be used for document classification.
<p>
This version of the method uses a default smoothing parameter of 1.0.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>input</code> - RDD of <code>(label, array of features)</code> pairs. Every vector should be a frequency
vector or a count vector.</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
<a name="train(org.apache.spark.rdd.RDD, double)">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>train</h4>
<pre>public static <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a> train(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> input,
double lambda)</pre>
<div class="block">Trains a Naive Bayes model given an RDD of <code>(label, features)</code> pairs.
<p>
This is the default Multinomial NB (<code>http://tinyurl.com/lsdw6p</code>) which can handle all
kinds of discrete data. For example, by converting documents into TF-IDF vectors, it
can be used for document classification.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>input</code> - RDD of <code>(label, array of features)</code> pairs. Every vector should be a frequency
vector or a count vector.</dd><dd><code>lambda</code> - The smoothing parameter</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
<a name="train(org.apache.spark.rdd.RDD, double, java.lang.String)">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>train</h4>
<pre>public static <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a> train(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> input,
double lambda,
java.lang.String modelType)</pre>
<div class="block">Trains a Naive Bayes model given an RDD of <code>(label, features)</code> pairs.
<p>
The model type can be set to either Multinomial NB (<code>http://tinyurl.com/lsdw6p</code>)
or Bernoulli NB (<code>http://tinyurl.com/p7c96j6</code>). The Multinomial NB can handle
discrete count data and can be called by setting the model type to "multinomial".
For example, it can be used with word counts or TF_IDF vectors of documents.
The Bernoulli model fits presence or absence (0-1) counts. By making every vector a
0-1 vector and setting the model type to "bernoulli", the fits and predicts as
Bernoulli NB.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>input</code> - RDD of <code>(label, array of features)</code> pairs. Every vector should be a frequency
vector or a count vector.</dd><dd><code>lambda</code> - The smoothing parameter
<p></dd><dd><code>modelType</code> - The type of NB model to fit from the enumeration NaiveBayesModels, can be
multinomial or bernoulli</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
<a name="setLambda(double)">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>setLambda</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html" title="class in org.apache.spark.mllib.classification">NaiveBayes</a> setLambda(double lambda)</pre>
<div class="block">Set the smoothing parameter. Default: 1.0.</div>
</li>
</ul>
<a name="getLambda()">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>getLambda</h4>
<pre>public double getLambda()</pre>
<div class="block">Get the smoothing parameter.</div>
</li>
</ul>
<a name="setModelType(java.lang.String)">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>setModelType</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayes.html" title="class in org.apache.spark.mllib.classification">NaiveBayes</a> setModelType(java.lang.String modelType)</pre>
<div class="block">Set the model type using a string (case-sensitive).
Supported options: "multinomial" (default) and "bernoulli".</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>modelType</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
<a name="getModelType()">
<!-- -->
</a>
<ul class="blockList">
<li class="blockList">
<h4>getModelType</h4>
<pre>public java.lang.String getModelType()</pre>
<div class="block">Get the model type.</div>
</li>
</ul>
<a name="run(org.apache.spark.rdd.RDD)">
<!-- -->
</a>
<ul class="blockListLast">
<li class="blockList">
<h4>run</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification">NaiveBayesModel</a> run(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> data)</pre>
<div class="block">Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - RDD of <a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression"><code>LabeledPoint</code></a>.</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
</div>
<!-- ========= END OF CLASS DATA ========= -->
<!-- ======= START OF BOTTOM NAVBAR ====== -->
<div class="bottomNav"><a name="navbar_bottom">
<!-- -->
</a><a href="#skip-navbar_bottom" title="Skip navigation links"></a><a name="navbar_bottom_firstrow">
<!-- -->
</a>
<ul class="navList" title="Navigation">
<li><a href="../../../../../overview-summary.html">Overview</a></li>
<li><a href="package-summary.html">Package</a></li>
<li class="navBarCell1Rev">Class</li>
<li><a href="package-tree.html">Tree</a></li>
<li><a href="../../../../../deprecated-list.html">Deprecated</a></li>
<li><a href="../../../../../index-all.html">Index</a></li>
<li><a href="../../../../../help-doc.html">Help</a></li>
</ul>
</div>
<div class="subNav">
<ul class="navList">
<li><a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification"><span class="strong">Prev Class</span></a></li>
<li><a href="../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html" title="class in org.apache.spark.mllib.classification"><span class="strong">Next Class</span></a></li>
</ul>
<ul class="navList">
<li><a href="../../../../../index.html?org/apache/spark/mllib/classification/NaiveBayes.html" target="_top">Frames</a></li>
<li><a href="NaiveBayes.html" target="_top">No Frames</a></li>
</ul>
<ul class="navList" id="allclasses_navbar_bottom">
<li><a href="../../../../../allclasses-noframe.html">All Classes</a></li>
</ul>
<div>
<script type="text/javascript"><!--
allClassesLink = document.getElementById("allclasses_navbar_bottom");
if(window==top) {
allClassesLink.style.display = "block";
}
else {
allClassesLink.style.display = "none";
}
//-->
</script>
</div>
<div>
<ul class="subNavList">
<li>Summary: </li>
<li>Nested | </li>
<li>Field | </li>
<li><a href="#constructor_summary">Constr</a> | </li>
<li><a href="#method_summary">Method</a></li>
</ul>
<ul class="subNavList">
<li>Detail: </li>
<li>Field | </li>
<li><a href="#constructor_detail">Constr</a> | </li>
<li><a href="#method_detail">Method</a></li>
</ul>
</div>
<a name="skip-navbar_bottom">
<!-- -->
</a></div>
<!-- ======== END OF BOTTOM NAVBAR ======= -->
<script defer="defer" type="text/javascript" src="../../../../../lib/jquery.js"></script><script defer="defer" type="text/javascript" src="../../../../../lib/api-javadocs.js"></script></body>
</html>