NaiveBayes

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19. 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 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. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (http://tinyurl.com/p7c96j6).

    input

    RDD of (label, array of features) pairs. Every vector should be a frequency vector or a count vector.

    lambda

    The smoothing parameter

20. 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 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. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (http://tinyurl.com/p7c96j6).

    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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