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## What changes were proposed in this pull request? Made DataFrame-based API primary * Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html * mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html * ml-guide.html includes a "maintenance mode" announcement about the RDD-based API * **Reviewers: please check this carefully** * (minor) Titles for DF API no longer include "- spark.ml" suffix. Titles for RDD API have "- RDD-based API" suffix * Moved migration guide to ml-guide from mllib-guide * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides * **Reviewers**: I did not change any of the content of the migration guides. Reorganized DataFrame-based guide: * ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc. * Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html * **Reviewers**: I did not change the content of these guides, except some intro text. * Sidebar remains the same, but with pipeline and tuning sections added Other: * ml-classification-regression.html: Moved text about linear methods to new section in page ## How was this patch tested? Generated docs locally Author: Joseph K. Bradley <joseph@databricks.com> Closes #14213 from jkbradley/ml-guide-2.0.
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| layout | title | displayTitle |
|---|---|---|
| global | Naive Bayes - RDD-based API | Naive Bayes - RDD-based API |
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.
spark.mllib supports multinomial naive
Bayes
and Bernoulli naive Bayes.
These models are typically used for document classification.
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 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
NaiveBayes implements
multinomial naive Bayes. It takes an RDD of
LabeledPoint and an optional
smoothing parameter lambda as input, an optional model type parameter (default is "multinomial"), and outputs a
NaiveBayesModel, which
can be used for evaluation and prediction.
Refer to the NaiveBayes Scala docs and NaiveBayesModel Scala docs for details on the API.
{% include_example scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala %}
NaiveBayes implements
multinomial naive Bayes. It takes a Scala RDD of
LabeledPoint and an
optionally smoothing parameter lambda as input, and output a
NaiveBayesModel, which
can be used for evaluation and prediction.
Refer to the NaiveBayes Java docs and NaiveBayesModel Java docs for details on the API.
{% include_example java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java %}
NaiveBayes implements multinomial
naive Bayes. It takes an RDD of
LabeledPoint and an optionally
smoothing parameter lambda as input, and output a
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 and NaiveBayesModel Python docs for more details on the API.
{% include_example python/mllib/naive_bayes_example.py %}