org.apache.spark.mllib.classification

NaiveBayes

object NaiveBayes extends Serializable

Top-level methods for calling naive Bayes.

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@Since( "0.9.0" )
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  19. 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 (http://tinyurl.com/lsdw6p) or Bernoulli NB (http://tinyurl.com/p7c96j6). 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" )
  20. 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 (http://tinyurl.com/lsdw6p) 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" )
  21. 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 (http://tinyurl.com/lsdw6p) 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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