Revision b8b80f6dea86d4e4a648b86e38936d3a82ffc0aa authored by Wenchen Fan on 20 June 2017, 16:15:33 UTC, committed by gatorsmile on 20 June 2017, 16:15:41 UTC
## What changes were proposed in this pull request?

This is a regression in Spark 2.2. In Spark 2.2, we introduced a new way to resolve persisted view: https://issues.apache.org/jira/browse/SPARK-18209 , but this makes the persisted view non case-preserving because we store the schema in hive metastore directly. We should follow data source table and store schema in table properties.

## How was this patch tested?

new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18360 from cloud-fan/view.

(cherry picked from commit e862dc904963cf7832bafc1d3d0ea9090bbddd81)
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
1 parent 514a7e6
Raw File
mllib-naive-bayes.md
---
layout: global
title: Naive Bayes - RDD-based API
displayTitle: Naive Bayes - RDD-based API
---

[Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) 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.

`spark.mllib` supports [multinomial naive
Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes)
and [Bernoulli naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html).
These models are typically used for [document classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html).
Within that context, each observation is a document and each
feature represents a term whose value is the frequency of the term (in multinomial naive Bayes) or
a zero or one indicating whether the term was found in the document (in Bernoulli naive Bayes).
Feature values must be nonnegative. The model type is selected with an optional parameter
"multinomial" or "bernoulli" with "multinomial" as the default.
[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, and sparse vectors should be supplied 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, an optional model type parameter (default is "multinomial"), and outputs a
[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
can be used for evaluation and prediction.

Refer to the [`NaiveBayes` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes) and [`NaiveBayesModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel) for details on the API.

{% include_example scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala %}
</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.

Refer to the [`NaiveBayes` Java docs](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) and [`NaiveBayesModel` Java docs](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html) for details on the API.

{% include_example java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java %}
</div>
<div data-lang="python" markdown="1">

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

Note that the Python API does not yet support model save/load but will in the future.

Refer to the [`NaiveBayes` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) and [`NaiveBayesModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel) for more details on the API.

{% include_example python/mllib/naive_bayes_example.py %}
</div>
</div>
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