Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4087#discussion_r26439275
--- 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 wouldn't call it inconsistent, but it does limit users who want static
methods. Adding new train() methods for every new parameter ended up hurting
us for trees & ensembles because the list became so long, so I'd still vote for
removing this static train method. We could even deprecate the other static
train method and tell users to use the builder pattern instead. If you feel
strongly about it, though, I'm OK with leaving it as is (since there are not
very many parameters for NB).
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