class NaiveBayes extends Serializable with Logging
Trains a Naive Bayes model given an RDD of (label, features)
pairs.
This is the 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. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (see here). The input feature values must be nonnegative.
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getLambda: Double
Get the smoothing parameter.
Get the smoothing parameter.
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getModelType: String
Get the model type.
Get the model type.
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def
run(data: RDD[LabeledPoint]): NaiveBayesModel
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
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def
setLambda(lambda: Double): NaiveBayes
Set the smoothing parameter.
Set the smoothing parameter. Default: 1.0.
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def
setModelType(modelType: String): NaiveBayes
Set the model type using a string (case-sensitive).
Set the model type using a string (case-sensitive). Supported options: "multinomial" (default) and "bernoulli".
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