[SPARK-11184][MLLIB] Declare most of .mllib code not-Experimental

Remove "Experimental" from .mllib code that has been around since 1.4.0 or earlier

Author: Sean Owen <sowen@cloudera.com>

Closes #9169 from srowen/SPARK-11184.

45 files changed, 43 insertions, 208 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index 85a413243b..5161bc7265 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -19,17 +19,15 @@ package org.apache.spark.mllib.classification
 
 import org.json4s.{DefaultFormats, JValue}
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
  * Represents a classification model that predicts to which of a set of categories an example
  * belongs. The categories are represented by double values: 0.0, 1.0, 2.0, etc.
  */
-@Experimental
 @Since("0.8.0")
 trait ClassificationModel extends Serializable {
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 5ceff5b225..2d52abc122 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.classification
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.classification.impl.GLMClassificationModel
 import org.apache.spark.mllib.linalg.BLAS.dot
 import org.apache.spark.mllib.linalg.{DenseVector, Vector}
@@ -82,35 +82,29 @@ class LogisticRegressionModel @Since("1.3.0") (
   private var threshold: Option[Double] = Some(0.5)
 
   /**
-   * :: Experimental ::
    * Sets the threshold that separates positive predictions from negative predictions
    * in Binary Logistic Regression. An example with prediction score greater than or equal to
    * this threshold is identified as an positive, and negative otherwise. The default value is 0.5.
    * It is only used for binary classification.
    */
   @Since("1.0.0")
-  @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
     this
   }
 
   /**
-   * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
    * It is only used for binary classification.
    */
   @Since("1.3.0")
-  @Experimental
   def getThreshold: Option[Double] = threshold
 
   /**
-   * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    * It is only used for binary classification.
    */
   @Since("1.0.0")
-  @Experimental
   def clearThreshold(): this.type = {
     threshold = None
     this
@@ -359,13 +353,11 @@ class LogisticRegressionWithLBFGS
   }
 
   /**
-   * :: Experimental ::
    * Set the number of possible outcomes for k classes classification problem in
    * Multinomial Logistic Regression.
    * By default, it is binary logistic regression so k will be set to 2.
    */
   @Since("1.3.0")
-  @Experimental
   def setNumClasses(numClasses: Int): this.type = {
     require(numClasses > 1)
     numOfLinearPredictor = numClasses - 1
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 896565cd90..a8d3fd4177 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.classification
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.classification.impl.GLMClassificationModel
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
@@ -43,32 +43,26 @@ class SVMModel @Since("1.1.0") (
   private var threshold: Option[Double] = Some(0.0)
 
   /**
-   * :: Experimental ::
    * Sets the threshold that separates positive predictions from negative predictions. An example
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
    */
   @Since("1.0.0")
-  @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
     this
   }
 
   /**
-   * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
    */
   @Since("1.3.0")
-  @Experimental
   def getThreshold: Option[Double] = threshold
 
   /**
-   * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    */
   @Since("1.0.0")
-  @Experimental
   def clearThreshold(): this.type = {
     threshold = None
     this
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
index 75630054d1..47bff5ebdd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
@@ -17,12 +17,11 @@
 
 package org.apache.spark.mllib.classification
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
 
 /**
- * :: Experimental ::
  * Train or predict a logistic regression model on streaming data. Training uses
  * Stochastic Gradient Descent to update the model based on each new batch of
  * incoming data from a DStream (see `LogisticRegressionWithSGD` for model equation)
@@ -43,7 +42,6 @@ import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
  *    .trainOn(DStream)
  * }}}
  */
-@Experimental
 @Since("1.3.0")
 class StreamingLogisticRegressionWithSGD private[mllib] (
     private var stepSize: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index f82bd82c20..7b203e2f40 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable.IndexedSeq
 
 import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, Vector => BV}
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, Matrices, Vector, Vectors}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
@@ -30,8 +30,6 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
 /**
- * :: Experimental ::
- *
  * This class performs expectation maximization for multivariate Gaussian
  * Mixture Models (GMMs).  A GMM represents a composite distribution of
  * independent Gaussian distributions with associated "mixing" weights
@@ -52,7 +50,6 @@ import org.apache.spark.util.Utils
  * is considered to have occurred.
  * @param maxIterations The maximum number of iterations to perform
  */
-@Experimental
 @Since("1.3.0")
 class GaussianMixture private (
     private var k: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index a5902190d4..2115f7d99c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Matrices, Matrix}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
@@ -33,8 +33,6 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{SQLContext, Row}
 
 /**
- * :: Experimental ::
- *
  * Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points
  * are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are
  * the respective mean and covariance for each Gaussian distribution i=1..k.
@@ -45,7 +43,6 @@ import org.apache.spark.sql.{SQLContext, Row}
  *                  the Multivariate Gaussian (Normal) Distribution for Gaussian i
  */
 @Since("1.3.0")
-@Experimental
 class GaussianMixtureModel @Since("1.3.0") (
   @Since("1.3.0") val weights: Array[Double],
   @Since("1.3.0") val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable {
@@ -132,7 +129,6 @@ class GaussianMixtureModel @Since("1.3.0") (
 }
 
 @Since("1.4.0")
-@Experimental
 object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
 
   private object SaveLoadV1_0 {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 92a321afb0..eb802a365e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import breeze.linalg.{DenseVector => BDV}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -28,8 +28,6 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
 /**
- * :: Experimental ::
- *
  * Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
  *
  * Terminology:
@@ -45,7 +43,6 @@ import org.apache.spark.util.Utils
  *       (Wikipedia)]]
  */
 @Since("1.3.0")
-@Experimental
 class LDA private (
     private var k: Int,
     private var maxIterations: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 15129e0dd5..31d8a9fdea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -35,14 +35,11 @@ import org.apache.spark.sql.{Row, SQLContext}
 import org.apache.spark.util.BoundedPriorityQueue
 
 /**
- * :: Experimental ::
- *
  * Latent Dirichlet Allocation (LDA) model.
  *
  * This abstraction permits for different underlying representations,
  * including local and distributed data structures.
  */
-@Experimental
 @Since("1.3.0")
 abstract class LDAModel private[clustering] extends Saveable {
 
@@ -184,15 +181,12 @@ abstract class LDAModel private[clustering] extends Saveable {
 }
 
 /**
- * :: Experimental ::
- *
  * Local LDA model.
  * This model stores only the inferred topics.
  * It may be used for computing topics for new documents, but it may give less accurate answers
  * than the [[DistributedLDAModel]].
  * @param topics Inferred topics (vocabSize x k matrix).
  */
-@Experimental
 @Since("1.3.0")
 class LocalLDAModel private[clustering] (
     @Since("1.3.0") val topics: Matrix,
@@ -481,14 +475,11 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
 }
 
 /**
- * :: Experimental ::
- *
  * Distributed LDA model.
  * This model stores the inferred topics, the full training dataset, and the topic distributions.
  * When computing topics for new documents, it may give more accurate answers
  * than the [[LocalLDAModel]].
  */
-@Experimental
 @Since("1.3.0")
 class DistributedLDAModel private[clustering] (
     private[clustering] val graph: Graph[LDA.TopicCounts, LDA.TokenCount],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index 6c76e26fd1..7cd9b08fa8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -21,7 +21,7 @@ import org.json4s.JsonDSL._
 import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
@@ -33,15 +33,12 @@ import org.apache.spark.util.random.XORShiftRandom
 import org.apache.spark.{Logging, SparkContext, SparkException}
 
 /**
- * :: Experimental ::
- *
  * Model produced by [[PowerIterationClustering]].
  *
  * @param k number of clusters
  * @param assignments an RDD of clustering [[PowerIterationClustering#Assignment]]s
  */
 @Since("1.3.0")
-@Experimental
 class PowerIterationClusteringModel @Since("1.3.0") (
     @Since("1.3.0") val k: Int,
     @Since("1.3.0") val assignments: RDD[PowerIterationClustering.Assignment])
@@ -107,8 +104,6 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
 }
 
 /**
- * :: Experimental ::
- *
  * Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by
  * [[http://www.icml2010.org/papers/387.pdf Lin and Cohen]]. From the abstract: PIC finds a very
  * low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise
@@ -120,7 +115,6 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
  *
  * @see [[http://en.wikipedia.org/wiki/Spectral_clustering Spectral clustering (Wikipedia)]]
  */
-@Experimental
 @Since("1.3.0")
 class PowerIterationClustering private[clustering] (
     private var k: Int,
@@ -239,17 +233,14 @@ class PowerIterationClustering private[clustering] (
 }
 
 @Since("1.3.0")
-@Experimental
 object PowerIterationClustering extends Logging {
 
   /**
-   * :: Experimental ::
    * Cluster assignment.
    * @param id node id
    * @param cluster assigned cluster id
    */
   @Since("1.3.0")
-  @Experimental
   case class Assignment(id: Long, cluster: Int)
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index 1d50ffec96..80843719f5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaSparkContext._
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.rdd.RDD
@@ -30,8 +30,6 @@ import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
 
 /**
- * :: Experimental ::
- *
  * StreamingKMeansModel extends MLlib's KMeansModel for streaming
  * algorithms, so it can keep track of a continuously updated weight
  * associated with each cluster, and also update the model by
@@ -65,7 +63,6 @@ import org.apache.spark.util.random.XORShiftRandom
  * as batches or points.
  */
 @Since("1.2.0")
-@Experimental
 class StreamingKMeansModel @Since("1.2.0") (
     @Since("1.2.0") override val clusterCenters: Array[Vector],
     @Since("1.2.0") val clusterWeights: Array[Double])
@@ -149,8 +146,6 @@ class StreamingKMeansModel @Since("1.2.0") (
 }
 
 /**
- * :: Experimental ::
- *
  * StreamingKMeans provides methods for configuring a
  * streaming k-means analysis, training the model on streaming,
  * and using the model to make predictions on streaming data.
@@ -168,7 +163,6 @@ class StreamingKMeansModel @Since("1.2.0") (
  * }}}
  */
 @Since("1.2.0")
-@Experimental
 class StreamingKMeans @Since("1.2.0") (
     @Since("1.2.0") var k: Int,
     @Since("1.2.0") var decayFactor: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index 508fe532b1..12cf220957 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -17,15 +17,13 @@
 
 package org.apache.spark.mllib.evaluation
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.Logging
-import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.evaluation.binary._
 import org.apache.spark.rdd.{RDD, UnionRDD}
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: Experimental ::
  * Evaluator for binary classification.
  *
  * @param scoreAndLabels an RDD of (score, label) pairs.
@@ -43,7 +41,6 @@ import org.apache.spark.sql.DataFrame
  *                partition boundaries.
  */
 @Since("1.0.0")
-@Experimental
 class BinaryClassificationMetrics @Since("1.3.0") (
     @Since("1.3.0") val scoreAndLabels: RDD[(Double, Double)],
     @Since("1.3.0") val numBins: Int) extends Logging {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index 00e837661d..c5104960cf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -19,8 +19,7 @@ package org.apache.spark.mllib.evaluation
 
 import scala.collection.Map
 
-import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{Matrices, Matrix}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
@@ -32,7 +31,6 @@ import org.apache.spark.sql.DataFrame
  * @param predictionAndLabels an RDD of (prediction, label) pairs.
  */
 @Since("1.1.0")
-@Experimental
 class MulticlassMetrics @Since("1.1.0") (predictionAndLabels: RDD[(Double, Double)]) {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
index a7f43f0b11..cc01936dd3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
@@ -23,7 +23,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.{JavaSparkContext, JavaRDD}
 import org.apache.spark.rdd.RDD
 
@@ -36,7 +36,6 @@ import org.apache.spark.rdd.RDD
  * @param predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs.
  */
 @Since("1.2.0")
-@Experimental
 class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])])
   extends Logging with Serializable {
 
@@ -159,7 +158,6 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
 
 }
 
-@Experimental
 object RankingMetrics {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 799ebb980e..1d8f4fe340 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.evaluation
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.rdd.RDD
 import org.apache.spark.Logging
 import org.apache.spark.mllib.linalg.Vectors
@@ -25,13 +25,11 @@ import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Multivariate
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: Experimental ::
  * Evaluator for regression.
  *
  * @param predictionAndObservations an RDD of (prediction, observation) pairs.
  */
 @Since("1.2.0")
-@Experimental
 class RegressionMetrics @Since("1.2.0") (
     predictionAndObservations: RDD[(Double, Double)]) extends Logging {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
index 5246faf221..d4d022afde 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
@@ -23,7 +23,7 @@ import org.json4s._
 import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.stat.Statistics
@@ -33,13 +33,11 @@ import org.apache.spark.SparkContext
 import org.apache.spark.sql.{SQLContext, Row}
 
 /**
- * :: Experimental ::
  * Chi Squared selector model.
  *
  * @param selectedFeatures list of indices to select (filter). Must be ordered asc
  */
 @Since("1.3.0")
-@Experimental
 class ChiSqSelectorModel @Since("1.3.0") (
   @Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer with Saveable {
 
@@ -173,7 +171,6 @@ object ChiSqSelectorModel extends Loader[ChiSqSelectorModel] {
 }
 
 /**
- * :: Experimental ::
  * Creates a ChiSquared feature selector.
  * @param numTopFeatures number of features that selector will select
  *                       (ordered by statistic value descending)
@@ -181,7 +178,6 @@ object ChiSqSelectorModel extends Loader[ChiSqSelectorModel] {
  *                       select all features.
  */
 @Since("1.3.0")
-@Experimental
 class ChiSqSelector @Since("1.3.0") (
   @Since("1.3.0") val numTopFeatures: Int) extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
index d0a6cf6168..c757fc7f06 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
@@ -17,18 +17,16 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg._
 
 /**
- * :: Experimental ::
  * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a
  * provided "weight" vector. In other words, it scales each column of the dataset by a scalar
  * multiplier.
  * @param scalingVec The values used to scale the reference vector's individual components.
  */
 @Since("1.4.0")
-@Experimental
 class ElementwiseProduct @Since("1.4.0") (
     @Since("1.4.0") val scalingVec: Vector) extends VectorTransformer {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
index e47d524b61..c93ed64183 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
@@ -22,20 +22,18 @@ import java.lang.{Iterable => JavaIterable}
 import scala.collection.JavaConverters._
 import scala.collection.mutable
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
 /**
- * :: Experimental ::
  * Maps a sequence of terms to their term frequencies using the hashing trick.
  *
  * @param numFeatures number of features (default: 2^20^)
  */
 @Since("1.1.0")
-@Experimental
 class HashingTF(val numFeatures: Int) extends Serializable {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
index 68078ccfa3..cffa9fba05 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
@@ -19,13 +19,12 @@ package org.apache.spark.mllib.feature
 
 import breeze.linalg.{DenseVector => BDV}
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
  * Inverse document frequency (IDF).
  * The standard formulation is used: `idf = log((m + 1) / (d(t) + 1))`, where `m` is the total
  * number of documents and `d(t)` is the number of documents that contain term `t`.
@@ -38,7 +37,6 @@ import org.apache.spark.rdd.RDD
  *                   should appear for filtering
  */
 @Since("1.1.0")
-@Experimental
 class IDF @Since("1.2.0") (@Since("1.2.0") val minDocFreq: Int) {
 
   @Since("1.1.0")
@@ -159,10 +157,8 @@ private object IDF {
 }
 
 /**
- * :: Experimental ::
  * Represents an IDF model that can transform term frequency vectors.
  */
-@Experimental
 @Since("1.1.0")
 class IDFModel private[spark] (@Since("1.1.0") val idf: Vector) extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index 8d5a22520d..af0c8e1d8a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 
 /**
- * :: Experimental ::
  * Normalizes samples individually to unit L^p^ norm
  *
  * For any 1 <= p < Double.PositiveInfinity, normalizes samples using
@@ -32,7 +31,6 @@ import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors
  * @param p Normalization in L^p^ space, p = 2 by default.
  */
 @Since("1.1.0")
-@Experimental
 class Normalizer @Since("1.1.0") (p: Double) extends VectorTransformer {
 
   @Since("1.1.0")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index f018b453ba..6fe573c528 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -18,13 +18,12 @@
 package org.apache.spark.mllib.feature
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
  * Standardizes features by removing the mean and scaling to unit std using column summary
  * statistics on the samples in the training set.
  *
@@ -33,7 +32,6 @@ import org.apache.spark.rdd.RDD
  * @param withStd True by default. Scales the data to unit standard deviation.
  */
 @Since("1.1.0")
-@Experimental
 class StandardScaler @Since("1.1.0") (withMean: Boolean, withStd: Boolean) extends Logging {
 
   @Since("1.1.0")
@@ -64,7 +62,6 @@ class StandardScaler @Since("1.1.0") (withMean: Boolean, withStd: Boolean) exten
 }
 
 /**
- * :: Experimental ::
  * Represents a StandardScaler model that can transform vectors.
  *
  * @param std column standard deviation values
@@ -73,7 +70,6 @@ class StandardScaler @Since("1.1.0") (withMean: Boolean, withStd: Boolean) exten
  * @param withMean whether to center the data before scaling
  */
 @Since("1.1.0")
-@Experimental
 class StandardScalerModel @Since("1.3.0") (
     @Since("1.3.0") val std: Vector,
     @Since("1.1.0") val mean: Vector,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index 58857c338f..f3e4d346e3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -31,15 +31,14 @@ import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
-import org.apache.spark.mllib.linalg.{Vector, Vectors, DenseMatrix, BLAS, DenseVector}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
 import org.apache.spark.rdd._
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
-import org.apache.spark.sql.{SQLContext, Row}
+import org.apache.spark.sql.SQLContext
 
 /**
  *  Entry in vocabulary
@@ -53,7 +52,6 @@ private case class VocabWord(
 )
 
 /**
- * :: Experimental ::
  * Word2Vec creates vector representation of words in a text corpus.
  * The algorithm first constructs a vocabulary from the corpus
  * and then learns vector representation of words in the vocabulary.
@@ -71,7 +69,6 @@ private case class VocabWord(
  * Distributed Representations of Words and Phrases and their Compositionality.
  */
 @Since("1.1.0")
-@Experimental
 class Word2Vec extends Serializable with Logging {
 
   private var vectorSize = 100
@@ -427,7 +424,6 @@ class Word2Vec extends Serializable with Logging {
 }
 
 /**
- * :: Experimental ::
  * Word2Vec model
  * @param wordIndex maps each word to an index, which can retrieve the corresponding
  *                  vector from wordVectors
@@ -435,7 +431,6 @@ class Word2Vec extends Serializable with Logging {
  *                    to the word mapped with index i can be retrieved by the slice
  *                    (i * vectorSize, i * vectorSize + vectorSize)
  */
-@Experimental
 @Since("1.1.0")
 class Word2VecModel private[mllib] (
     private val wordIndex: Map[String, Int],
@@ -558,7 +553,6 @@ class Word2VecModel private[mllib] (
 }
 
 @Since("1.4.0")
-@Experimental
 object Word2VecModel extends Loader[Word2VecModel] {
 
   private def buildWordIndex(model: Map[String, Array[Float]]): Map[String, Int] = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index aea5c4f8a8..70ef1ed30c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -25,7 +25,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.{HashPartitioner, Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.fpm.FPGrowth._
@@ -33,15 +33,11 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
 /**
- * :: Experimental ::
- *
  * Model trained by [[FPGrowth]], which holds frequent itemsets.
  * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
- *
  */
 @Since("1.3.0")
-@Experimental
 class FPGrowthModel[Item: ClassTag] @Since("1.3.0") (
     @Since("1.3.0") val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
   /**
@@ -56,8 +52,6 @@ class FPGrowthModel[Item: ClassTag] @Since("1.3.0") (
 }
 
 /**
- * :: Experimental ::
- *
  * A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in
  * [[http://dx.doi.org/10.1145/1454008.1454027 Li et al., PFP: Parallel FP-Growth for Query
  *  Recommendation]]. PFP distributes computation in such a way that each worker executes an
@@ -74,7 +68,6 @@ class FPGrowthModel[Item: ClassTag] @Since("1.3.0") (
  *
  */
 @Since("1.3.0")
-@Experimental
 class FPGrowth private (
     private var minSupport: Double,
     private var numPartitions: Int) extends Logging with Serializable {
@@ -213,12 +206,7 @@ class FPGrowth private (
   }
 }
 
-/**
- * :: Experimental ::
- *
- */
 @Since("1.3.0")
-@Experimental
 object FPGrowth {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index 4dcf8f28f2..4591cb88ef 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -20,11 +20,9 @@ package org.apache.spark.mllib.linalg
 import org.apache.spark.annotation.{Experimental, Since}
 
 /**
- * :: Experimental ::
  * Represents singular value decomposition (SVD) factors.
  */
 @Since("1.0.0")
-@Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index 81a6c0550b..09527dcf5d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import breeze.linalg.{DenseMatrix => BDM}
 
 import org.apache.spark.{Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Matrix, SparseMatrix}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -115,8 +115,6 @@ private[mllib] object GridPartitioner {
 }
 
 /**
- * :: Experimental ::
- *
  * Represents a distributed matrix in blocks of local matrices.
  *
  * @param blocks The RDD of sub-matrix blocks ((blockRowIndex, blockColIndex), sub-matrix) that
@@ -132,7 +130,6 @@ private[mllib] object GridPartitioner {
  *              zero, the number of columns will be calculated when `numCols` is invoked.
  */
 @Since("1.3.0")
-@Experimental
 class BlockMatrix @Since("1.3.0") (
     @Since("1.3.0") val blocks: RDD[((Int, Int), Matrix)],
     @Since("1.3.0") val rowsPerBlock: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 644f293d88..8a70f34e70 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -19,23 +19,20 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
 
 /**
- * :: Experimental ::
  * Represents an entry in an distributed matrix.
  * @param i row index
  * @param j column index
  * @param value value of the entry
  */
 @Since("1.0.0")
-@Experimental
 case class MatrixEntry(i: Long, j: Long, value: Double)
 
 /**
- * :: Experimental ::
  * Represents a matrix in coordinate format.
  *
  * @param entries matrix entries
@@ -45,7 +42,6 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  *              columns will be determined by the max column index plus one.
  */
 @Since("1.0.0")
-@Experimental
 class CoordinateMatrix @Since("1.0.0") (
     @Since("1.0.0") val entries: RDD[MatrixEntry],
     private var nRows: Long,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index b20ea0dc50..e6af0c0ec7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -19,21 +19,18 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.SingularValueDecomposition
 
 /**
- * :: Experimental ::
  * Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
  */
 @Since("1.0.0")
-@Experimental
 case class IndexedRow(index: Long, vector: Vector)
 
 /**
- * :: Experimental ::
  * Represents a row-oriented [[org.apache.spark.mllib.linalg.distributed.DistributedMatrix]] with
  * indexed rows.
  *
@@ -44,7 +41,6 @@ case class IndexedRow(index: Long, vector: Vector)
  *              columns will be determined by the size of the first row.
  */
 @Since("1.0.0")
-@Experimental
 class IndexedRowMatrix @Since("1.0.0") (
     @Since("1.0.0") val rows: RDD[IndexedRow],
     private var nRows: Long,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index b8a7adceb1..52c0f19c64 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -26,8 +26,7 @@ import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, SparseVector => BS
 import breeze.numerics.{sqrt => brzSqrt}
 
 import org.apache.spark.Logging
-import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 import org.apache.spark.rdd.RDD
@@ -35,7 +34,6 @@ import org.apache.spark.util.random.XORShiftRandom
 import org.apache.spark.storage.StorageLevel
 
 /**
- * :: Experimental ::
  * Represents a row-oriented distributed Matrix with no meaningful row indices.
  *
  * @param rows rows stored as an RDD[Vector]
@@ -45,7 +43,6 @@ import org.apache.spark.storage.StorageLevel
  *              columns will be determined by the size of the first row.
  */
 @Since("1.0.0")
-@Experimental
 class RowMatrix @Since("1.0.0") (
     @Since("1.0.0") val rows: RDD[Vector],
     private var nRows: Long,
@@ -676,7 +673,6 @@ class RowMatrix @Since("1.0.0") (
 }
 
 @Since("1.0.0")
-@Experimental
 object RowMatrix {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
index 41d7c4d355..b0a716936a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.random
 import scala.reflect.ClassTag
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD, JavaSparkContext}
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.linalg.Vector
@@ -29,10 +29,8 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
 /**
- * :: Experimental ::
  * Generator methods for creating RDDs comprised of `i.i.d.` samples from some distribution.
  */
-@Experimental
 @Since("1.1.0")
 object RandomRDDs {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index 877d31ba41..ec78ea2453 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -29,7 +29,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -37,8 +37,6 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
 
 /**
- * :: Experimental ::
- *
  * Regression model for isotonic regression.
  *
  * @param boundaries Array of boundaries for which predictions are known.
@@ -49,7 +47,6 @@ import org.apache.spark.sql.SQLContext
  *
  */
 @Since("1.3.0")
-@Experimental
 class IsotonicRegressionModel @Since("1.3.0") (
     @Since("1.3.0") val boundaries: Array[Double],
     @Since("1.3.0") val predictions: Array[Double],
@@ -233,8 +230,6 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
 }
 
 /**
- * :: Experimental ::
- *
  * Isotonic regression.
  * Currently implemented using parallelized pool adjacent violators algorithm.
  * Only univariate (single feature) algorithm supported.
@@ -252,7 +247,6 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
  *
  * @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]
  */
-@Experimental
 @Since("1.3.0")
 class IsotonicRegression private (private var isotonic: Boolean) extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
index 0e72d6591c..a95a54225a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
@@ -19,13 +19,12 @@ package org.apache.spark.mllib.regression
 
 import org.json4s.{DefaultFormats, JValue}
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 
 @Since("0.8.0")
-@Experimental
 trait RegressionModel extends Serializable {
   /**
    * Predict values for the given data set using the model trained.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index fe1d487cdd..fe2a46b9ee 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 
 /**
- * :: Experimental ::
  * Train or predict a linear regression model on streaming data. Training uses
  * Stochastic Gradient Descent to update the model based on each new batch of
  * incoming data from a DStream (see `LinearRegressionWithSGD` for model equation)
@@ -40,7 +39,6 @@ import org.apache.spark.mllib.linalg.Vector
  *    .setInitialWeights(Vectors.dense(...))
  *    .trainOn(DStream)
  */
-@Experimental
 @Since("1.1.0")
 class StreamingLinearRegressionWithSGD private[mllib] (
     private var stepSize: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
index 4a856f7f34..f253963270 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
@@ -19,12 +19,11 @@ package org.apache.spark.mllib.stat
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
  * Kernel density estimation. Given a sample from a population, estimate its probability density
  * function at each of the given evaluation points using kernels. Only Gaussian kernel is supported.
  *
@@ -39,7 +38,6 @@ import org.apache.spark.rdd.RDD
  * }}}
  */
 @Since("1.4.0")
-@Experimental
 class KernelDensity extends Serializable {
 
   import KernelDensity._
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index 84d64a5bfb..bcb33a7a04 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.stat
 
 import scala.annotation.varargs
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.{JavaRDD, JavaDoubleRDD}
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Matrix, Vector}
@@ -30,11 +30,9 @@ import org.apache.spark.mllib.stat.test.{ChiSqTest, ChiSqTestResult, KolmogorovS
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
  * API for statistical functions in MLlib.
  */
 @Since("1.1.0")
-@Experimental
 object Statistics {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
index b0916d3e84..8a29fd39a9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
@@ -20,11 +20,9 @@ package org.apache.spark.mllib.stat.test
 import org.apache.spark.annotation.{Experimental, Since}
 
 /**
- * :: Experimental ::
  * Trait for hypothesis test results.
  * @tparam DF Return type of `degreesOfFreedom`.
  */
-@Experimental
 @Since("1.1.0")
 trait TestResult[DF] {
 
@@ -79,10 +77,8 @@ trait TestResult[DF] {
 }
 
 /**
- * :: Experimental ::
  * Object containing the test results for the chi-squared hypothesis test.
  */
-@Experimental
 @Since("1.1.0")
 class ChiSqTestResult private[stat] (override val pValue: Double,
     @Since("1.1.0") override val degreesOfFreedom: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 53d6482f80..af1f7e74c0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -21,7 +21,7 @@ import scala.collection.JavaConverters._
 import scala.collection.mutable
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.RandomForest.NodeIndexInfo
@@ -36,7 +36,6 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom
 
 /**
- * :: Experimental ::
  * A class which implements a decision tree learning algorithm for classification and regression.
  * It supports both continuous and categorical features.
  * @param strategy The configuration parameters for the tree algorithm which specify the type
@@ -44,7 +43,6 @@ import org.apache.spark.util.random.XORShiftRandom
  *                 categorical), depth of the tree, quantile calculation strategy, etc.
  */
 @Since("1.0.0")
-@Experimental
 class DecisionTree @Since("1.0.0") (private val strategy: Strategy)
   extends Serializable with Logging {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index 66a07e3136..729a211574 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.impl.PeriodicRDDCheckpointer
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -31,7 +31,6 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
 /**
- * :: Experimental ::
  * A class that implements
  * [[http://en.wikipedia.org/wiki/Gradient_boosting  Stochastic Gradient Boosting]]
  * for regression and binary classification.
@@ -50,7 +49,6 @@ import org.apache.spark.storage.StorageLevel
  * @param boostingStrategy Parameters for the gradient boosting algorithm.
  */
 @Since("1.2.0")
-@Experimental
 class GradientBoostedTrees @Since("1.2.0") (private val boostingStrategy: BoostingStrategy)
   extends Serializable with Logging {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index 63a902f3eb..a684cdd18c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable
 import scala.collection.JavaConverters._
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Strategy
@@ -39,7 +39,6 @@ import org.apache.spark.util.Utils
 import org.apache.spark.util.random.SamplingUtils
 
 /**
- * :: Experimental ::
  * A class that implements a [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]]
  * learning algorithm for classification and regression.
  * It supports both continuous and categorical features.
@@ -66,7 +65,6 @@ import org.apache.spark.util.random.SamplingUtils
  *                                  to "onethird" for regression.
  * @param seed Random seed for bootstrapping and choosing feature subsets.
  */
-@Experimental
 private class RandomForest (
     private val strategy: Strategy,
     private val numTrees: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index fc13bcfd8e..d2513a9d5c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -19,12 +19,11 @@ package org.apache.spark.mllib.tree.configuration
 
 import scala.beans.BeanProperty
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
 
 /**
- * :: Experimental ::
  * Configuration options for [[org.apache.spark.mllib.tree.GradientBoostedTrees]].
  *
  * @param treeStrategy Parameters for the tree algorithm. We support regression and binary
@@ -47,7 +46,6 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  *                      [[org.apache.spark.mllib.tree.GradientBoostedTrees.run()]] is used.
  */
 @Since("1.2.0")
-@Experimental
 case class BoostingStrategy @Since("1.4.0") (
     // Required boosting parameters
     @Since("1.2.0") @BeanProperty var treeStrategy: Strategy,
@@ -79,7 +77,6 @@ case class BoostingStrategy @Since("1.4.0") (
 }
 
 @Since("1.2.0")
-@Experimental
 object BoostingStrategy {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
index 4e0cd473de..1470295d8a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 
 /**
- * :: Experimental ::
  * Enum to describe whether a feature is "continuous" or "categorical"
  */
 @Since("1.0.0")
-@Experimental
 object FeatureType extends Enumeration {
   @Since("1.0.0")
   type FeatureType = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
index 8262db8a4f..1c16f136eb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 
 /**
- * :: Experimental ::
  * Enum for selecting the quantile calculation strategy
  */
 @Since("1.0.0")
-@Experimental
 object QuantileStrategy extends Enumeration {
   @Since("1.0.0")
   type QuantileStrategy = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 89cc13b7c0..372d6617a4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -20,13 +20,12 @@ package org.apache.spark.mllib.tree.configuration
 import scala.beans.BeanProperty
 import scala.collection.JavaConverters._
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.tree.impurity.{Variance, Entropy, Gini, Impurity}
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 
 /**
- * :: Experimental ::
  * Stores all the configuration options for tree construction
  * @param algo  Learning goal.  Supported:
  *              [[org.apache.spark.mllib.tree.configuration.Algo.Classification]],
@@ -68,7 +67,6 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                           [[org.apache.spark.SparkContext]], this setting is ignored.
  */
 @Since("1.0.0")
-@Experimental
 class Strategy @Since("1.3.0") (
     @Since("1.0.0") @BeanProperty var algo: Algo,
     @Since("1.0.0") @BeanProperty var impurity: Impurity,
@@ -179,7 +177,6 @@ class Strategy @Since("1.3.0") (
 }
 
 @Since("1.2.0")
-@Experimental
 object Strategy {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala
index aac84243d5..70afaa162b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala
@@ -19,12 +19,9 @@ package org.apache.spark.mllib.tree.impl
 
 import scala.collection.mutable.{HashMap => MutableHashMap}
 
-import org.apache.spark.annotation.Experimental
-
 /**
  * Time tracker implementation which holds labeled timers.
  */
-@Experimental
 private[spark] class TimeTracker extends Serializable {
 
   private val starts: MutableHashMap[String, Long] = new MutableHashMap[String, Long]()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index e1bf23f4c3..54c136aecf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.tree.configuration.{Algo, FeatureType}
@@ -35,14 +35,12 @@ import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 import org.apache.spark.util.Utils
 
 /**
- * :: Experimental ::
  * Decision tree model for classification or regression.
  * This model stores the decision tree structure and parameters.
  * @param topNode root node
  * @param algo algorithm type -- classification or regression
  */
 @Since("1.0.0")
-@Experimental
 class DecisionTreeModel @Since("1.0.0") (
     @Since("1.0.0") val topNode: Node,
     @Since("1.0.0") val algo: Algo) extends Serializable with Saveable {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index df5b8feab5..90e032e3d9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -25,7 +25,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -38,16 +38,13 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.util.Utils
 
-
 /**
- * :: Experimental ::
  * Represents a random forest model.
  *
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
  */
 @Since("1.2.0")
-@Experimental
 class RandomForestModel @Since("1.2.0") (
     @Since("1.2.0") override val algo: Algo,
     @Since("1.2.0") override val trees: Array[DecisionTreeModel])
@@ -108,7 +105,6 @@ object RandomForestModel extends Loader[RandomForestModel] {
 }
 
 /**
- * :: Experimental ::
  * Represents a gradient boosted trees model.
  *
  * @param algo algorithm for the ensemble model, either Classification or Regression
@@ -116,7 +112,6 @@ object RandomForestModel extends Loader[RandomForestModel] {
  * @param treeWeights tree ensemble weights
  */
 @Since("1.2.0")
-@Experimental
 class GradientBoostedTreesModel @Since("1.2.0") (
     @Since("1.2.0") override val algo: Algo,
     @Since("1.2.0") override val trees: Array[DecisionTreeModel],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 81c2f0ce6e..414ea99cfd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -19,9 +19,7 @@ package org.apache.spark.mllib.util
 
 import scala.reflect.ClassTag
 
-import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
-
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.PartitionwiseSampledRDD
@@ -30,8 +28,6 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS.dot
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.streaming.dstream.DStream
 
 /**
  * Helper methods to load, save and pre-process data used in ML Lib.
@@ -263,13 +259,11 @@ object MLUtils {
   }
 
   /**
-   * :: Experimental ::
    * Return a k element array of pairs of RDDs with the first element of each pair
    * containing the training data, a complement of the validation data and the second
    * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.
    */
   @Since("1.0.0")
-  @Experimental
   def kFold[T: ClassTag](rdd: RDD[T], numFolds: Int, seed: Int): Array[(RDD[T], RDD[T])] = {
     val numFoldsF = numFolds.toFloat
     (1 to numFolds).map { fold =>