Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4087#discussion_r26169266
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -262,4 +303,58 @@ object NaiveBayes {
def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
new NaiveBayes(lambda).run(input)
}
+
+
+ /**
+ * 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.
+ *
+ * @param input RDD of `(label, array of features)` pairs. Every vector
should be a frequency
+ * vector or a count vector.
+ * @param lambda The smoothing parameter
+ *
+ * @param modelType The type of NB model to fit from the enumeration
NaiveBayesModels, can be
+ * multinomial or bernoulli
+ */
+ def train(input: RDD[LabeledPoint], lambda: Double, modelType: String):
NaiveBayesModel = {
--- End diff --
I'm actually wondering if we even need to provide this static train method.
Users can use the builder pattern new
NaiveBayes().setLambda().setModelType().run() instead. Could you please remove
it? Sorry for not noticing this earlier!
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