NaiveBayes

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12. final def ne(arg0: AnyRef): Boolean

    

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17. 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" )

18. 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" )

19. 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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    @Since( "0.9.0" )

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21. final def wait(arg0: Long, arg1: Int): Unit

    

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22. final def wait(arg0: Long): Unit

    

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