Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: mllib-naive-bayes.md

layout: global title: Naive Bayes - RDD-based API displayTitle: Naive Bayes - RDD-based API license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. 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/NaiveBayesclassifier#MultinomialnaiveBayes) and . These models are typically used for . 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. 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. <div class="codetabs"> <div data-lang="python" markdown="1"> implements multinomial naive Bayes. It takes an RDD of and an optionally smoothing parameter `lambda` as input, and output a , 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 and for more details on the API. {% includeexample python/mllib/naivebayes_example.py %} </div> <div data-lang="scala" markdown="1"> implements multinomial naive Bayes. It takes an RDD of and an optional smoothing parameter `lambda` as input, an optional model type parameter (default is "multinomial"), and outputs a , which can be used for evaluation and prediction. Refer to the and for details on the API. {% include_example scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala %} </div> <div data-lang="java" markdown="1"> implements multinomial naive Bayes. It takes a Scala RDD of and an optionally smoothing parameter `lambda` as input, and output a , which can be used for evaluation and prediction. Refer to the and for details on the API. {% include_example java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java %} </div> </div>