[SPARK-9665] [MLLIB] audit MLlib API annotations

I only found `ml.NaiveBayes` missing `Experimental` annotation. This PR doesn't cover Python APIs.

cc jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8452 from mengxr/SPARK-9665.

mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala

@@ -18,11 +18,11 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkException
-import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
-import org.apache.spark.ml.param.{ParamMap, ParamValidators, Param, DoubleParam}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.PredictorParams
+import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes}
-import org.apache.spark.mllib.classification.{NaiveBayesModel => OldNaiveBayesModel}
+import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes, NaiveBayesModel => OldNaiveBayesModel}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
@@ -59,6 +59,7 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
 }
 
 /**
+ * :: Experimental ::
  * Naive Bayes Classifiers.
  * It supports both Multinomial NB
  * ([[http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html]])
@@ -68,6 +69,7 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
  * ([[http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html]]).
  * The input feature values must be nonnegative.
  */
+@Experimental
 class NaiveBayes(override val uid: String)
   extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel]
   with NaiveBayesParams {
@@ -101,11 +103,13 @@ class NaiveBayes(override val uid: String)
 }
 
 /**
+ * :: Experimental ::
  * Model produced by [[NaiveBayes]]
  * @param pi log of class priors, whose dimension is C (number of classes)
  * @param theta log of class conditional probabilities, whose dimension is C (number of classes)
  *              by D (number of features)
  */
+@Experimental
 class NaiveBayesModel private[ml] (
     override val uid: String,
     val pi: Vector,