<commit_before>---
layout: global
title: Naive Bayes - MLlib
displayTitle: <a href="mllib-guide.html">MLlib</a> - Naive Bayes
---

Naive Bayes is a simple multiclass classification algorithm with the assumption of independence
between every pair of features. Naive Bayes can be trained very efficiently. Within a single pass to
the training data, it computes the conditional probability distribution of each feature given label,
and then it applies Bayes' theorem to compute the conditional probability distribution of label
given an observation and use it for prediction. For more details, please visit the Wikipedia page
[Naive Bayes classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier).

In MLlib, we implemented multinomial naive Bayes, which is typically used for document
classification. Within that context, each observation is a document, each feature represents a term,
whose value is the frequency of the term. For its formulation, please visit the Wikipedia page
[Multinomial Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes)
or the section
[Naive Bayes text classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html)
from the book Introduction to Information
Retrieval. [Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by
setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature
vectors are usually sparse. Please supply sparse vectors as input to take advantage of
sparsity. Since the training data is only used once, it is not necessary to cache it.

## Examples

<div class="codetabs">
<div data-lang="scala" markdown="1">

[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
multinomial naive Bayes. It takes an RDD of
[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
smoothing parameter `lambda` as input, and output a
[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
can be used for evaluation and prediction.

{% highlight scala %}
import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint

val data = sc.textFile("mllib/data/sample_naive_bayes_data.txt")
val parsedData = data.map { line =>
  val parts = line.split(',')
  LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
}
// Split data into training (60%) and test (40%).
val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
val training = splits(0)
val test = splits(1)

val model = NaiveBayes.train(training, lambda = 1.0)
val prediction = model.predict(test.map(_.features))

val predictionAndLabel = prediction.zip(test.map(_.label))
val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
{% endhighlight %}
</div>

<div data-lang="java" markdown="1">

[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements
multinomial naive Bayes. It takes a Scala RDD of
[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an
optionally smoothing parameter `lambda` as input, and output a
[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), which
can be used for evaluation and prediction.

{% highlight java %}
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.classification.NaiveBayes;
import org.apache.spark.mllib.classification.NaiveBayesModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import scala.Tuple2;

JavaRDD<LabeledPoint> training = ... // training set
JavaRDD<LabeledPoint> test = ... // test set

final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);

JavaRDD<Double> prediction =
  test.map(new Function<LabeledPoint, Double>() {
    @Override public Double call(LabeledPoint p) {
      return model.predict(p.features());
    }
  });
JavaPairRDD<Double, Double> predictionAndLabel = 
  prediction.zip(test.map(new Function<LabeledPoint, Double>() {
    @Override public Double call(LabeledPoint p) {
      return p.label();
    }
  }));
double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
    @Override public Boolean call(Tuple2<Double, Double> pl) {
      return pl._1() == pl._2();
    }
  }).count() / test.count();
{% endhighlight %}
</div>

<div data-lang="python" markdown="1">
<commit_msg>SPARK-2293. Replace RDD.zip usage by map with predict inside.<commit_after>---
layout: global
title: Naive Bayes - MLlib
displayTitle: <a href="mllib-guide.html">MLlib</a> - Naive Bayes
---

Naive Bayes is a simple multiclass classification algorithm with the assumption of independence
between every pair of features. Naive Bayes can be trained very efficiently. Within a single pass to
the training data, it computes the conditional probability distribution of each feature given label,
and then it applies Bayes' theorem to compute the conditional probability distribution of label
given an observation and use it for prediction. For more details, please visit the Wikipedia page
[Naive Bayes classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier).

In MLlib, we implemented multinomial naive Bayes, which is typically used for document
classification. Within that context, each observation is a document, each feature represents a term,
whose value is the frequency of the term. For its formulation, please visit the Wikipedia page
[Multinomial Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes)
or the section
[Naive Bayes text classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html)
from the book Introduction to Information
Retrieval. [Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by
setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature
vectors are usually sparse. Please supply sparse vectors as input to take advantage of
sparsity. Since the training data is only used once, it is not necessary to cache it.

## Examples

<div class="codetabs">
<div data-lang="scala" markdown="1">

[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
multinomial naive Bayes. It takes an RDD of
[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
smoothing parameter `lambda` as input, and output a
[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
can be used for evaluation and prediction.

{% highlight scala %}
import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint

val data = sc.textFile("mllib/data/sample_naive_bayes_data.txt")
val parsedData = data.map { line =>
  val parts = line.split(',')
  LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
}
// Split data into training (60%) and test (40%).
val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
val training = splits(0)
val test = splits(1)

val model = NaiveBayes.train(training, lambda = 1.0)

val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
{% endhighlight %}
</div>

<div data-lang="java" markdown="1">

[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements
multinomial naive Bayes. It takes a Scala RDD of
[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an
optionally smoothing parameter `lambda` as input, and output a
[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), which
can be used for evaluation and prediction.

{% highlight java %}
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.classification.NaiveBayes;
import org.apache.spark.mllib.classification.NaiveBayesModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import scala.Tuple2;

JavaRDD<LabeledPoint> training = ... // training set
JavaRDD<LabeledPoint> test = ... // test set

final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);

JavaPairRDD<Double, Double> predictionAndLabel = 
  test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
    @Override public Tuple2<Double, Double> call(LabeledPoint p) {
      return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
    }
  });
double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
    @Override public Boolean call(Tuple2<Double, Double> pl) {
      return pl._1() == pl._2();
    }
  }).count() / test.count();
{% endhighlight %}
</div>

<div data-lang="python" markdown="1">

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