Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3626#discussion_r21564191
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -65,6 +66,25 @@ class NaiveBayesModel private[mllib] (
override def predict(testData: Vector): Double = {
labels(brzArgmax(brzPi + brzTheta * testData.toBreeze))
}
+
+ def classProbabilities(testData: RDD[Vector]):
--- End diff --
Classes are index from 0, so would it work to return (for each instance) a
Vector of probabilities rather than a Map[Double, Double]? That seems simpler
(and more efficient).
Also, I have a PR [https://github.com/apache/spark/pull/3637] for a
separate part of MLlib which adds a method like this for predicting class
conditional probabilities. I'd like us to use the same name for the prediction
method, but I'm open about choosing a name. I had used "predictProbabilities"
to (a) have it start with "predict" like other prediction methods and (b) leave
open the possibility of supporting a similar method for regression algorithms
(which can predict probability distributions). But I'll agree
"classProbabilities" is more specific. Do you have strong preferences?
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