object NaiveBayes extends Serializable
Top-level methods for calling naive Bayes.
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def
train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel
Trains a Naive Bayes model given an RDD of
(label, features)pairs.Trains a Naive Bayes model given an RDD of
(label, features)pairs.The model type can be set to either Multinomial NB (see here) or Bernoulli NB (see here). 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.
- input
RDD of
(label, array of features)pairs. Every vector should be a frequency vector or a count vector.- lambda
The smoothing parameter
- modelType
The type of NB model to fit from the enumeration NaiveBayesModels, can be multinomial or bernoulli
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- @Since( "1.4.0" )
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def
train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel
Trains a Naive Bayes model given an RDD of
(label, features)pairs.Trains a Naive Bayes model given an RDD of
(label, features)pairs.This is the default Multinomial NB (see here) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification.
- input
RDD of
(label, array of features)pairs. Every vector should be a frequency vector or a count vector.- lambda
The smoothing parameter
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- @Since( "0.9.0" )
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def
train(input: RDD[LabeledPoint]): NaiveBayesModel
Trains a Naive Bayes model given an RDD of
(label, features)pairs.Trains a Naive Bayes model given an RDD of
(label, features)pairs.This is the default Multinomial NB (see here) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification.
This version of the method uses a default smoothing parameter of 1.0.
- input
RDD of
(label, array of features)pairs. Every vector should be a frequency vector or a count vector.
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