[SPARK-7748] [MLLIB] Graduate spark.ml from alpha

With descent coverage of feature transformers, algorithms, and model tuning support, it is time to graduate `spark.ml` from alpha. This PR changes all `AlphaComponent` annotations to either `DeveloperApi` or `Experimental`, depending on whether we expect a class/method to be used by end users (who use the pipeline API to assemble/tune their ML pipelines but not to create new pipeline components.) `UnaryTransformer` becomes a `DeveloperApi` in this PR.

jkbradley harsha2010

Author: Xiangrui Meng <meng@databricks.com>

Closes #6417 from mengxr/SPARK-7748 and squashes the following commits:

effbccd [Xiangrui Meng] organize imports
c15028e [Xiangrui Meng] added missing docs
1b2e5f8 [Xiangrui Meng] update package doc
73ca791 [Xiangrui Meng] alpha -> ex/dev for the rest
93819db [Xiangrui Meng] alpha -> ex/dev in ml.param
55ca073 [Xiangrui Meng] alpha -> ex/dev in ml.feature
83572f1 [Xiangrui Meng] add Experimental and DeveloperApi tags (wip)

43 files changed, 267 insertions, 201 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala
index 9e16e60270..e9a5d7c0e7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala
@@ -19,15 +19,15 @@ package org.apache.spark.ml
 
 import scala.annotation.varargs
 
-import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.param.{ParamMap, ParamPair, Params}
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.param.{ParamMap, ParamPair}
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  * Abstract class for estimators that fit models to data.
  */
-@AlphaComponent
+@DeveloperApi
 abstract class Estimator[M <: Model[M]] extends PipelineStage {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala
index 70e7495ac6..186bf7ae7a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Model.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Model.scala
@@ -17,16 +17,16 @@
 
 package org.apache.spark.ml
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param.ParamMap
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  * A fitted model, i.e., a [[Transformer]] produced by an [[Estimator]].
  *
  * @tparam M model type
  */
-@AlphaComponent
+@DeveloperApi
 abstract class Model[M <: Model[M]] extends Transformer {
   /**
    * The parent estimator that produced this model.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
index 43bee1b770..9da3ff65c7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -20,17 +20,17 @@ package org.apache.spark.ml
 import scala.collection.mutable.ListBuffer
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.ml.param.{Param, ParamMap, Params}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.types.StructType
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  * A stage in a pipeline, either an [[Estimator]] or a [[Transformer]].
  */
-@AlphaComponent
+@DeveloperApi
 abstract class PipelineStage extends Params with Logging {
 
   /**
@@ -69,7 +69,7 @@ abstract class PipelineStage extends Params with Logging {
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each
  * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline#fit]] is called, the
  * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator#fit]] method will
@@ -80,7 +80,7 @@ abstract class PipelineStage extends Params with Logging {
  * transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as
  * an identity transformer.
  */
-@AlphaComponent
+@Experimental
 class Pipeline(override val uid: String) extends Estimator[PipelineModel] {
 
   def this() = this(Identifiable.randomUID("pipeline"))
@@ -169,10 +169,10 @@ class Pipeline(override val uid: String) extends Estimator[PipelineModel] {
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Represents a fitted pipeline.
  */
-@AlphaComponent
+@Experimental
 class PipelineModel private[ml] (
     override val uid: String,
     val stages: Array[Transformer])
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
index ec0f76aa66..e752b81a14 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -58,7 +58,6 @@ private[ml] trait PredictorParams extends Params
 
 /**
  * :: DeveloperApi ::
- *
  * Abstraction for prediction problems (regression and classification).
  *
  * @tparam FeaturesType  Type of features.
@@ -113,7 +112,6 @@ abstract class Predictor[
    *
    * The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.
    */
-  @DeveloperApi
   private[ml] def featuresDataType: DataType = new VectorUDT
 
   override def transformSchema(schema: StructType): StructType = {
@@ -134,7 +132,6 @@ abstract class Predictor[
 
 /**
  * :: DeveloperApi ::
- *
  * Abstraction for a model for prediction tasks (regression and classification).
  *
  * @tparam FeaturesType  Type of features.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
index 38bb6a5a53..f07f733a5d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml
 import scala.annotation.varargs
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.sql.DataFrame
@@ -28,10 +28,10 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  * Abstract class for transformers that transform one dataset into another.
  */
-@AlphaComponent
+@DeveloperApi
 abstract class Transformer extends PipelineStage {
 
   /**
@@ -73,10 +73,12 @@ abstract class Transformer extends PipelineStage {
 }
 
 /**
+ * :: DeveloperApi ::
  * Abstract class for transformers that take one input column, apply transformation, and output the
  * result as a new column.
  */
-private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]
+@DeveloperApi
+abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]
   extends Transformer with HasInputCol with HasOutputCol with Logging {
 
   /** @group setParam */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala
index f5f37aa779..457c15830f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala
@@ -19,10 +19,12 @@ package org.apache.spark.ml.attribute
 
 import scala.collection.mutable.ArrayBuffer
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField}
 
 /**
+ * :: DeveloperApi ::
  * Attributes that describe a vector ML column.
  *
  * @param name name of the attribute group (the ML column name)
@@ -31,6 +33,7 @@ import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField}
  * @param attrs optional array of attributes. Attribute will be copied with their corresponding
  *              indices in the array.
  */
+@DeveloperApi
 class AttributeGroup private (
     val name: String,
     val numAttributes: Option[Int],
@@ -182,7 +185,11 @@ class AttributeGroup private (
   }
 }
 
-/** Factory methods to create attribute groups. */
+/**
+ * :: DeveloperApi ::
+ * Factory methods to create attribute groups.
+ */
+@DeveloperApi
 object AttributeGroup {
 
   import AttributeKeys._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala
index a83febd7de..5c7089b491 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala
@@ -17,12 +17,17 @@
 
 package org.apache.spark.ml.attribute
 
+import org.apache.spark.annotation.DeveloperApi
+
 /**
+ * :: DeveloperApi ::
  * An enum-like type for attribute types: [[AttributeType$#Numeric]], [[AttributeType$#Nominal]],
  * and [[AttributeType$#Binary]].
  */
+@DeveloperApi
 sealed abstract class AttributeType(val name: String)
 
+@DeveloperApi
 object AttributeType {
 
   /** Numeric type. */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
index e8f7f15278..ce43a450da 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
@@ -19,11 +19,14 @@ package org.apache.spark.ml.attribute
 
 import scala.annotation.varargs
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.types.{DoubleType, Metadata, MetadataBuilder, StructField}
 
 /**
+ * :: DeveloperApi ::
  * Abstract class for ML attributes.
  */
+@DeveloperApi
 sealed abstract class Attribute extends Serializable {
 
   name.foreach { n =>
@@ -135,6 +138,10 @@ private[attribute] trait AttributeFactory {
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 object Attribute extends AttributeFactory {
 
   private[attribute] override def fromMetadata(metadata: Metadata): Attribute = {
@@ -163,6 +170,7 @@ object Attribute extends AttributeFactory {
 
 
 /**
+ * :: DeveloperApi ::
  * A numeric attribute with optional summary statistics.
  * @param name optional name
  * @param index optional index
@@ -171,6 +179,7 @@ object Attribute extends AttributeFactory {
  * @param std optional standard deviation
  * @param sparsity optional sparsity (ratio of zeros)
  */
+@DeveloperApi
 class NumericAttribute private[ml] (
     override val name: Option[String] = None,
     override val index: Option[Int] = None,
@@ -278,8 +287,10 @@ class NumericAttribute private[ml] (
 }
 
 /**
+ * :: DeveloperApi ::
  * Factory methods for numeric attributes.
  */
+@DeveloperApi
 object NumericAttribute extends AttributeFactory {
 
   /** The default numeric attribute. */
@@ -298,6 +309,7 @@ object NumericAttribute extends AttributeFactory {
 }
 
 /**
+ * :: DeveloperApi ::
  * A nominal attribute.
  * @param name optional name
  * @param index optional index
@@ -306,6 +318,7 @@ object NumericAttribute extends AttributeFactory {
  *                  defined.
  * @param values optional values. At most one of `numValues` and `values` can be defined.
  */
+@DeveloperApi
 class NominalAttribute private[ml] (
     override val name: Option[String] = None,
     override val index: Option[Int] = None,
@@ -430,7 +443,11 @@ class NominalAttribute private[ml] (
   }
 }
 
-/** Factory methods for nominal attributes. */
+/**
+ * :: DeveloperApi ::
+ * Factory methods for nominal attributes.
+ */
+@DeveloperApi
 object NominalAttribute extends AttributeFactory {
 
   /** The default nominal attribute. */
@@ -450,11 +467,13 @@ object NominalAttribute extends AttributeFactory {
 }
 
 /**
+ * :: DeveloperApi ::
  * A binary attribute.
  * @param name optional name
  * @param index optional index
  * @param values optionla values. If set, its size must be 2.
  */
+@DeveloperApi
 class BinaryAttribute private[ml] (
     override val name: Option[String] = None,
     override val index: Option[Int] = None,
@@ -526,7 +545,11 @@ class BinaryAttribute private[ml] (
   }
 }
 
-/** Factory methods for binary attributes. */
+/**
+ * :: DeveloperApi ::
+ * Factory methods for binary attributes.
+ */
+@DeveloperApi
 object BinaryAttribute extends AttributeFactory {
 
   /** The default binary attribute. */
@@ -543,8 +566,10 @@ object BinaryAttribute extends AttributeFactory {
 }
 
 /**
+ * :: DeveloperApi ::
  * An unresolved attribute.
  */
+@DeveloperApi
 object UnresolvedAttribute extends Attribute {
 
   override def attrType: AttributeType = AttributeType.Unresolved
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 7c961332bf..8030e0728a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.ml.classification
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{TreeClassifierParams, DecisionTreeParams, DecisionTreeModel, Node}
+import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -31,14 +31,13 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm
  * for classification.
  * It supports both binary and multiclass labels, as well as both continuous and categorical
  * features.
  */
-@AlphaComponent
+@Experimental
 final class DecisionTreeClassifier(override val uid: String)
   extends Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
   with DecisionTreeParams with TreeClassifierParams {
@@ -89,19 +88,19 @@ final class DecisionTreeClassifier(override val uid: String)
   }
 }
 
+@Experimental
 object DecisionTreeClassifier {
   /** Accessor for supported impurities: entropy, gini */
   final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for classification.
  * It supports both binary and multiclass labels, as well as both continuous and categorical
  * features.
  */
-@AlphaComponent
+@Experimental
 final class DecisionTreeClassificationModel private[ml] (
     override val uid: String,
     override val rootNode: Node)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index d504d84beb..d8592eb2d9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -20,11 +20,11 @@ package org.apache.spark.ml.classification
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
-import org.apache.spark.ml.tree.{GBTParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeClassifierParams, TreeEnsembleModel}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -36,14 +36,13 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
  * learning algorithm for classification.
  * It supports binary labels, as well as both continuous and categorical features.
  * Note: Multiclass labels are not currently supported.
  */
-@AlphaComponent
+@Experimental
 final class GBTClassifier(override val uid: String)
   extends Predictor[Vector, GBTClassifier, GBTClassificationModel]
   with GBTParams with TreeClassifierParams with Logging {
@@ -144,6 +143,7 @@ final class GBTClassifier(override val uid: String)
   }
 }
 
+@Experimental
 object GBTClassifier {
   // The losses below should be lowercase.
   /** Accessor for supported loss settings: logistic */
@@ -151,8 +151,7 @@ object GBTClassifier {
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
  * model for classification.
  * It supports binary labels, as well as both continuous and categorical features.
@@ -160,7 +159,7 @@ object GBTClassifier {
  * @param _trees  Decision trees in the ensemble.
  * @param _treeWeights  Weights for the decision trees in the ensemble.
  */
-@AlphaComponent
+@Experimental
 final class GBTClassificationModel(
     override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel],
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 8694c96e4c..d13109d9da 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -19,11 +19,11 @@ package org.apache.spark.ml.classification
 
 import scala.collection.mutable
 
-import breeze.linalg.{norm => brzNorm, DenseVector => BDV}
-import breeze.optimize.{LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
-import breeze.optimize.{CachedDiffFunction, DiffFunction}
+import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
+import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.Identifiable
@@ -35,7 +35,6 @@ import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.{SparkException, Logging}
 
 /**
  * Params for logistic regression.
@@ -45,12 +44,11 @@ private[classification] trait LogisticRegressionParams extends ProbabilisticClas
   with HasThreshold
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Logistic regression.
  * Currently, this class only supports binary classification.
  */
-@AlphaComponent
+@Experimental
 class LogisticRegression(override val uid: String)
   extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel]
   with LogisticRegressionParams with Logging {
@@ -221,11 +219,10 @@ class LogisticRegression(override val uid: String)
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Model produced by [[LogisticRegression]].
  */
-@AlphaComponent
+@Experimental
 class LogisticRegressionModel private[ml] (
     override val uid: String,
     val weights: Vector,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index 1543f051cc..36735cd834 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -21,7 +21,7 @@ import java.util.UUID
 
 import scala.language.existentials
 
-import org.apache.spark.annotation.{AlphaComponent, Experimental}
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.param.Param
@@ -54,8 +54,7 @@ private[ml] trait OneVsRestParams extends PredictorParams {
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Model produced by [[OneVsRest]].
  * This stores the models resulting from training k binary classifiers: one for each class.
  * Each example is scored against all k models, and the model with the highest score
@@ -67,7 +66,7 @@ private[ml] trait OneVsRestParams extends PredictorParams {
  *               The i-th model is produced by testing the i-th class (taking label 1) vs the rest
  *               (taking label 0).
  */
-@AlphaComponent
+@Experimental
 final class OneVsRestModel private[ml] (
     override val uid: String,
     labelMetadata: Metadata,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index a1de791985..67600ebd7b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -19,10 +19,10 @@ package org.apache.spark.ml.classification
 
 import scala.collection.mutable
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{RandomForestParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -33,14 +33,13 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]] learning algorithm for
  * classification.
  * It supports both binary and multiclass labels, as well as both continuous and categorical
  * features.
  */
-@AlphaComponent
+@Experimental
 final class RandomForestClassifier(override val uid: String)
   extends Predictor[Vector, RandomForestClassifier, RandomForestClassificationModel]
   with RandomForestParams with TreeClassifierParams {
@@ -100,6 +99,7 @@ final class RandomForestClassifier(override val uid: String)
   }
 }
 
+@Experimental
 object RandomForestClassifier {
   /** Accessor for supported impurity settings: entropy, gini */
   final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
@@ -110,15 +110,14 @@ object RandomForestClassifier {
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]] model for classification.
  * It supports both binary and multiclass labels, as well as both continuous and categorical
  * features.
  * @param _trees  Decision trees in the ensemble.
  *               Warning: These have null parents.
  */
-@AlphaComponent
+@Experimental
 final class RandomForestClassificationModel private[ml] (
     override val uid: String,
     private val _trees: Array[DecisionTreeClassificationModel])
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index ddbdd00ceb..f695ddaeef 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -17,8 +17,7 @@
 
 package org.apache.spark.ml.evaluation
 
-import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.evaluation.Evaluator
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
@@ -28,11 +27,10 @@ import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.types.DoubleType
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Evaluator for binary classification, which expects two input columns: score and label.
  */
-@AlphaComponent
+@Experimental
 class BinaryClassificationEvaluator(override val uid: String)
   extends Evaluator with HasRawPredictionCol with HasLabelCol {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
index cabd1c97c0..61e937e693 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.ml.evaluation
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  * Abstract class for evaluators that compute metrics from predictions.
  */
-@AlphaComponent
+@DeveloperApi
 abstract class Evaluator extends Params {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
index 80458928c5..1771177e1e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.evaluation
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param.{Param, ParamValidators}
 import org.apache.spark.ml.param.shared.{HasLabelCol, HasPredictionCol}
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
@@ -26,11 +26,10 @@ import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.types.DoubleType
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Evaluator for regression, which expects two input columns: prediction and label.
  */
-@AlphaComponent
+@Experimental
 final class RegressionEvaluator(override val uid: String)
   extends Evaluator with HasPredictionCol with HasLabelCol {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
index 62f4a63434..b06122d733 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.BinaryAttribute
 import org.apache.spark.ml.param._
@@ -28,10 +28,10 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DoubleType, StructType}
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Binarize a column of continuous features given a threshold.
  */
-@AlphaComponent
+@Experimental
 final class Binarizer(override val uid: String)
   extends Transformer with HasInputCol with HasOutputCol {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
index ac8dfb5632..a3d1f6f65c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.feature
 import java.{util => ju}
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Model
 import org.apache.spark.ml.attribute.NominalAttribute
 import org.apache.spark.ml.param._
@@ -31,10 +31,10 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * `Bucketizer` maps a column of continuous features to a column of feature buckets.
  */
-@AlphaComponent
+@Experimental
 final class Bucketizer(override val uid: String)
   extends Model[Bucketizer] with HasInputCol with HasOutputCol {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
index 8b32eee0e4..3ae1833390 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param.Param
 import org.apache.spark.ml.util.Identifiable
@@ -26,12 +26,12 @@ import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.types.DataType
 
 /**
- * :: AlphaComponent ::
+ * :: 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.
  */
-@AlphaComponent
+@Experimental
 class ElementwiseProduct(override val uid: String)
   extends UnaryTransformer[Vector, Vector, ElementwiseProduct] {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
index 8942d45219..f936aef80f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
@@ -17,22 +17,22 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.AttributeGroup
-import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.param.{IntParam, ParamValidators}
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.feature
 import org.apache.spark.sql.DataFrame
-import org.apache.spark.sql.functions.{udf, col}
+import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.sql.types.{ArrayType, StructType}
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Maps a sequence of terms to their term frequencies using the hashing trick.
  */
-@AlphaComponent
+@Experimental
 class HashingTF(override val uid: String) extends Transformer with HasInputCol with HasOutputCol {
 
   def this() = this(Identifiable.randomUID("hashingTF"))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
index 788c392050..376b84530c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
@@ -58,10 +58,10 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Compute the Inverse Document Frequency (IDF) given a collection of documents.
  */
-@AlphaComponent
+@Experimental
 final class IDF(override val uid: String) extends Estimator[IDFModel] with IDFBase {
 
   def this() = this(Identifiable.randomUID("idf"))
@@ -85,10 +85,10 @@ final class IDF(override val uid: String) extends Estimator[IDFModel] with IDFBa
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Model fitted by [[IDF]].
  */
-@AlphaComponent
+@Experimental
 class IDFModel private[ml] (
     override val uid: String,
     idfModel: feature.IDFModel)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
index 3f689d1585..8282e5ffa1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param.{DoubleParam, ParamValidators}
 import org.apache.spark.ml.util.Identifiable
@@ -26,10 +26,10 @@ import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.types.DataType
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Normalize a vector to have unit norm using the given p-norm.
  */
-@AlphaComponent
+@Experimental
 class Normalizer(override val uid: String) extends UnaryTransformer[Vector, Vector, Normalizer] {
 
   def this() = this(Identifiable.randomUID("normalizer"))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
index 1fb9b9ae75..eb6ec49f85 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
@@ -18,16 +18,17 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.attribute.{Attribute, BinaryAttribute, NominalAttribute}
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
 
 /**
+ * :: Experimental ::
  * A one-hot encoder that maps a column of label indices to a column of binary vectors, with
  * at most a single one-value. By default, the binary vector has an element for each category, so
  * with 5 categories, an input value of 2.0 would map to an output vector of
@@ -36,7 +37,7 @@ import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
  * of 0.0 would map to a vector of all zeros. Including the first category makes the vector columns
  * linearly dependent because they sum up to one.
  */
-@AlphaComponent
+@Experimental
 class OneHotEncoder(override val uid: String)
   extends UnaryTransformer[Double, Vector, OneHotEncoder] with HasInputCol with HasOutputCol {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
index 8ddf9d6a1e..442e958202 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ml.feature
 
 import scala.collection.mutable
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param.{IntParam, ParamValidators}
 import org.apache.spark.ml.util.Identifiable
@@ -27,14 +27,14 @@ import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.types.DataType
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion,
  * which is available at [[http://en.wikipedia.org/wiki/Polynomial_expansion]], "In mathematics, an
  * expansion of a product of sums expresses it as a sum of products by using the fact that
  * multiplication distributes over addition". Take a 2-variable feature vector as an example:
  * `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`.
  */
-@AlphaComponent
+@Experimental
 class PolynomialExpansion(override val uid: String)
   extends UnaryTransformer[Vector, Vector, PolynomialExpansion] {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
index 5ccda15d87..fdd2494fc8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
@@ -51,11 +51,11 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Standardizes features by removing the mean and scaling to unit variance using column summary
  * statistics on the samples in the training set.
  */
-@AlphaComponent
+@Experimental
 class StandardScaler(override val uid: String) extends Estimator[StandardScalerModel]
   with StandardScalerParams {
 
@@ -95,10 +95,10 @@ class StandardScaler(override val uid: String) extends Estimator[StandardScalerM
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Model fitted by [[StandardScaler]].
  */
-@AlphaComponent
+@Experimental
 class StandardScalerModel private[ml] (
     override val uid: String,
     scaler: feature.StandardScalerModel)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 3f79b67309..a2dc8a8b96 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.attribute.NominalAttribute
 import org.apache.spark.ml.param._
@@ -52,13 +52,13 @@ private[feature] trait StringIndexerBase extends Params with HasInputCol with Ha
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * A label indexer that maps a string column of labels to an ML column of label indices.
  * If the input column is numeric, we cast it to string and index the string values.
  * The indices are in [0, numLabels), ordered by label frequencies.
  * So the most frequent label gets index 0.
  */
-@AlphaComponent
+@Experimental
 class StringIndexer(override val uid: String) extends Estimator[StringIndexerModel]
   with StringIndexerBase {
 
@@ -86,10 +86,10 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Model fitted by [[StringIndexer]].
  */
-@AlphaComponent
+@Experimental
 class StringIndexerModel private[ml] (
     override val uid: String,
     labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
index 31f3a1aa4c..21c15b6c33 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
@@ -17,19 +17,19 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * A tokenizer that converts the input string to lowercase and then splits it by white spaces.
  *
  * @see [[RegexTokenizer]]
  */
-@AlphaComponent
+@Experimental
 class Tokenizer(override val uid: String) extends UnaryTransformer[String, Seq[String], Tokenizer] {
 
   def this() = this(Identifiable.randomUID("tok"))
@@ -46,13 +46,13 @@ class Tokenizer(override val uid: String) extends UnaryTransformer[String, Seq[S
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * A regex based tokenizer that extracts tokens either by using the provided regex pattern to split
  * the text (default) or repeatedly matching the regex (if `gaps` is true).
  * Optional parameters also allow filtering tokens using a minimal length.
  * It returns an array of strings that can be empty.
  */
-@AlphaComponent
+@Experimental
 class RegexTokenizer(override val uid: String)
   extends UnaryTransformer[String, Seq[String], RegexTokenizer] {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 181b62f46f..514ffb03c0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.feature
 import scala.collection.mutable.ArrayBuilder
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.Identifiable
@@ -30,10 +30,10 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * A feature transformer that merges multiple columns into a vector column.
  */
-@AlphaComponent
+@Experimental
 class VectorAssembler(override val uid: String)
   extends Transformer with HasInputCols with HasOutputCol {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index e238fb310e..1d0f23b4fb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -22,7 +22,7 @@ import java.util.{Map => JMap}
 
 import scala.collection.JavaConverters._
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.param.{IntParam, ParamValidators, Params}
@@ -56,8 +56,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Class for indexing categorical feature columns in a dataset of [[Vector]].
  *
  * This has 2 usage modes:
@@ -91,7 +90,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
  *  - Add warning if a categorical feature has only 1 category.
  *  - Add option for allowing unknown categories.
  */
-@AlphaComponent
+@Experimental
 class VectorIndexer(override val uid: String) extends Estimator[VectorIndexerModel]
   with VectorIndexerParams {
 
@@ -230,8 +229,7 @@ private object VectorIndexer {
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Transform categorical features to use 0-based indices instead of their original values.
  *  - Categorical features are mapped to indices.
  *  - Continuous features (columns) are left unchanged.
@@ -246,7 +244,7 @@ private object VectorIndexer {
  *                      Values are maps from original features values to 0-based category indices.
  *                      If a feature is not in this map, it is treated as continuous.
  */
-@AlphaComponent
+@Experimental
 class VectorIndexerModel private[ml] (
     override val uid: String,
     val numFeatures: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index ed03266922..36f19509f0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
@@ -82,11 +82,11 @@ private[feature] trait Word2VecBase extends Params
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further
  * natural language processing or machine learning process.
  */
-@AlphaComponent
+@Experimental
 final class Word2Vec(override val uid: String) extends Estimator[Word2VecModel] with Word2VecBase {
 
   def this() = this(Identifiable.randomUID("w2v"))
@@ -135,10 +135,10 @@ final class Word2Vec(override val uid: String) extends Estimator[Word2VecModel]
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Model fitted by [[Word2Vec]].
  */
-@AlphaComponent
+@Experimental
 class Word2VecModel private[ml] (
     override val uid: String,
     wordVectors: feature.Word2VecModel)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/package-info.java b/mllib/src/main/scala/org/apache/spark/ml/package-info.java
index 00d9c802e9..87f4223964 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/package-info.java
+++ b/mllib/src/main/scala/org/apache/spark/ml/package-info.java
@@ -16,10 +16,10 @@
  */
 
 /**
- * Spark ML is an ALPHA component that adds a new set of machine learning APIs to let users quickly
+ * Spark ML is a BETA component that adds a new set of machine learning APIs to let users quickly
  * assemble and configure practical machine learning pipelines.
  */
-@AlphaComponent
+@Experimental
 package org.apache.spark.ml;
 
-import org.apache.spark.annotation.AlphaComponent;
+import org.apache.spark.annotation.Experimental;
diff --git a/mllib/src/main/scala/org/apache/spark/ml/package.scala b/mllib/src/main/scala/org/apache/spark/ml/package.scala
index ac75e9de1a..c589d06d9f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/package.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/package.scala
@@ -18,7 +18,7 @@
 package org.apache.spark
 
 /**
- * Spark ML is an ALPHA component that adds a new set of machine learning APIs to let users quickly
+ * Spark ML is a BETA component that adds a new set of machine learning APIs to let users quickly
  * assemble and configure practical machine learning pipelines.
  *
  * @groupname param Parameters
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 12fc5b561f..1afa59c994 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -24,11 +24,11 @@ import scala.annotation.varargs
 import scala.collection.mutable
 import scala.collection.JavaConverters._
 
-import org.apache.spark.annotation.{DeveloperApi, AlphaComponent}
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.ml.util.Identifiable
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  * A param with self-contained documentation and optionally default value. Primitive-typed param
  * should use the specialized versions, which are more friendly to Java users.
  *
@@ -39,7 +39,7 @@ import org.apache.spark.ml.util.Identifiable
  *                See [[ParamValidators]] for factory methods for common validation functions.
  * @tparam T param value type
  */
-@AlphaComponent
+@DeveloperApi
 class Param[T](val parent: String, val name: String, val doc: String, val isValid: T => Boolean)
   extends Serializable {
 
@@ -174,7 +174,11 @@ object ParamValidators {
 
 // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ...
 
-/** Specialized version of [[Param[Double]]] for Java. */
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Double]]] for Java.
+ */
+@DeveloperApi
 class DoubleParam(parent: String, name: String, doc: String, isValid: Double => Boolean)
   extends Param[Double](parent, name, doc, isValid) {
 
@@ -189,7 +193,11 @@ class DoubleParam(parent: String, name: String, doc: String, isValid: Double =>
   override def w(value: Double): ParamPair[Double] = super.w(value)
 }
 
-/** Specialized version of [[Param[Int]]] for Java. */
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Int]]] for Java.
+ */
+@DeveloperApi
 class IntParam(parent: String, name: String, doc: String, isValid: Int => Boolean)
   extends Param[Int](parent, name, doc, isValid) {
 
@@ -204,7 +212,11 @@ class IntParam(parent: String, name: String, doc: String, isValid: Int => Boolea
   override def w(value: Int): ParamPair[Int] = super.w(value)
 }
 
-/** Specialized version of [[Param[Float]]] for Java. */
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Float]]] for Java.
+ */
+@DeveloperApi
 class FloatParam(parent: String, name: String, doc: String, isValid: Float => Boolean)
   extends Param[Float](parent, name, doc, isValid) {
 
@@ -219,7 +231,11 @@ class FloatParam(parent: String, name: String, doc: String, isValid: Float => Bo
   override def w(value: Float): ParamPair[Float] = super.w(value)
 }
 
-/** Specialized version of [[Param[Long]]] for Java. */
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Long]]] for Java.
+ */
+@DeveloperApi
 class LongParam(parent: String, name: String, doc: String, isValid: Long => Boolean)
   extends Param[Long](parent, name, doc, isValid) {
 
@@ -234,7 +250,11 @@ class LongParam(parent: String, name: String, doc: String, isValid: Long => Bool
   override def w(value: Long): ParamPair[Long] = super.w(value)
 }
 
-/** Specialized version of [[Param[Boolean]]] for Java. */
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Boolean]]] for Java.
+ */
+@DeveloperApi
 class BooleanParam(parent: String, name: String, doc: String) // No need for isValid
   extends Param[Boolean](parent, name, doc) {
 
@@ -243,7 +263,11 @@ class BooleanParam(parent: String, name: String, doc: String) // No need for isV
   override def w(value: Boolean): ParamPair[Boolean] = super.w(value)
 }
 
-/** Specialized version of [[Param[Array[String]]]] for Java. */
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Array[String]]]] for Java.
+ */
+@DeveloperApi
 class StringArrayParam(parent: Params, name: String, doc: String, isValid: Array[String] => Boolean)
   extends Param[Array[String]](parent, name, doc, isValid) {
 
@@ -256,7 +280,11 @@ class StringArrayParam(parent: Params, name: String, doc: String, isValid: Array
   def w(value: java.util.List[String]): ParamPair[Array[String]] = w(value.asScala.toArray)
 }
 
-/** Specialized version of [[Param[Array[Double]]]] for Java. */
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Array[Double]]]] for Java.
+ */
+@DeveloperApi
 class DoubleArrayParam(parent: Params, name: String, doc: String, isValid: Array[Double] => Boolean)
   extends Param[Array[Double]](parent, name, doc, isValid) {
 
@@ -270,8 +298,10 @@ class DoubleArrayParam(parent: Params, name: String, doc: String, isValid: Array
 }
 
 /**
+ * :: Experimental ::
  * A param amd its value.
  */
+@Experimental
 case class ParamPair[T](param: Param[T], value: T) {
   // This is *the* place Param.validate is called.  Whenever a parameter is specified, we should
   // always construct a ParamPair so that validate is called.
@@ -279,11 +309,11 @@ case class ParamPair[T](param: Param[T], value: T) {
 }
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  * Trait for components that take parameters. This also provides an internal param map to store
  * parameter values attached to the instance.
  */
-@AlphaComponent
+@DeveloperApi
 trait Params extends Identifiable with Serializable {
 
   /**
@@ -541,10 +571,10 @@ trait Params extends Identifiable with Serializable {
 abstract class JavaParams extends Params
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * A param to value map.
  */
-@AlphaComponent
+@Experimental
 final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any])
   extends Serializable {
 
@@ -665,6 +695,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any])
   def size: Int = map.size
 }
 
+@Experimental
 object ParamMap {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index 2a5ddbfae5..900b637ff8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -31,7 +31,7 @@ import org.apache.hadoop.fs.{FileSystem, Path}
 import org.netlib.util.intW
 
 import org.apache.spark.{Logging, Partitioner}
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
@@ -169,8 +169,10 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
 }
 
 /**
+ * :: Experimental ::
  * Model fitted by ALS.
  */
+@Experimental
 class ALSModel private[ml] (
     override val uid: String,
     k: Int,
@@ -208,6 +210,7 @@ class ALSModel private[ml] (
 
 
 /**
+ * :: Experimental ::
  * Alternating Least Squares (ALS) matrix factorization.
  *
  * ALS attempts to estimate the ratings matrix `R` as the product of two lower-rank matrices,
@@ -236,6 +239,7 @@ class ALSModel private[ml] (
  * indicated user
  * preferences rather than explicit ratings given to items.
  */
+@Experimental
 class ALS(override val uid: String) extends Estimator[ALSModel] with ALSParams {
 
   import org.apache.spark.ml.recommendation.ALS.Rating
@@ -326,7 +330,11 @@ class ALS(override val uid: String) extends Estimator[ALSModel] with ALSParams {
 @DeveloperApi
 object ALS extends Logging {
 
-  /** Rating class for better code readability. */
+  /**
+   * :: DeveloperApi ::
+   * Rating class for better code readability.
+   */
+  @DeveloperApi
   case class Rating[@specialized(Int, Long) ID](user: ID, item: ID, rating: Float)
 
   /** Trait for least squares solvers applied to the normal equation. */
@@ -487,8 +495,10 @@ object ALS extends Logging {
   }
 
   /**
+   * :: DeveloperApi ::
    * Implementation of the ALS algorithm.
    */
+  @DeveloperApi
   def train[ID: ClassTag]( // scalastyle:ignore
       ratings: RDD[Rating[ID]],
       rank: Int = 10,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index e67df21b2e..43b68e7bb2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.ml.regression
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{TreeRegressorParams, DecisionTreeParams, DecisionTreeModel, Node}
+import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeRegressorParams}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -31,13 +31,12 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm
  * for regression.
  * It supports both continuous and categorical features.
  */
-@AlphaComponent
+@Experimental
 final class DecisionTreeRegressor(override val uid: String)
   extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]
   with DecisionTreeParams with TreeRegressorParams {
@@ -79,19 +78,19 @@ final class DecisionTreeRegressor(override val uid: String)
   }
 }
 
+@Experimental
 object DecisionTreeRegressor {
   /** Accessor for supported impurities: variance */
   final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for regression.
  * It supports both continuous and categorical features.
  * @param rootNode  Root of the decision tree
  */
-@AlphaComponent
+@Experimental
 final class DecisionTreeRegressionModel private[ml] (
     override val uid: String,
     override val rootNode: Node)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 4249ff5c1e..69f4f5414c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -20,10 +20,10 @@ package org.apache.spark.ml.regression
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
-import org.apache.spark.ml.tree.{GBTParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeEnsembleModel, TreeRegressorParams}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -35,13 +35,12 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
  * learning algorithm for regression.
  * It supports both continuous and categorical features.
  */
-@AlphaComponent
+@Experimental
 final class GBTRegressor(override val uid: String)
   extends Predictor[Vector, GBTRegressor, GBTRegressionModel]
   with GBTParams with TreeRegressorParams with Logging {
@@ -134,6 +133,7 @@ final class GBTRegressor(override val uid: String)
   }
 }
 
+@Experimental
 object GBTRegressor {
   // The losses below should be lowercase.
   /** Accessor for supported loss settings: squared (L2), absolute (L1) */
@@ -141,7 +141,7 @@ object GBTRegressor {
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  *
  * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
  * model for regression.
@@ -149,7 +149,7 @@ object GBTRegressor {
  * @param _trees  Decision trees in the ensemble.
  * @param _treeWeights  Weights for the decision trees in the ensemble.
  */
-@AlphaComponent
+@Experimental
 final class GBTRegressionModel(
     override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel],
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 3ebb78f792..7c40db1a40 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -23,7 +23,7 @@ import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
 import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol}
@@ -44,8 +44,7 @@ private[regression] trait LinearRegressionParams extends PredictorParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Linear regression.
  *
  * The learning objective is to minimize the squared error, with regularization.
@@ -58,7 +57,7 @@ private[regression] trait LinearRegressionParams extends PredictorParams
  *  - L1 (Lasso)
  *  - L2 + L1 (elastic net)
  */
-@AlphaComponent
+@Experimental
 class LinearRegression(override val uid: String)
   extends Regressor[Vector, LinearRegression, LinearRegressionModel]
   with LinearRegressionParams with Logging {
@@ -190,11 +189,10 @@ class LinearRegression(override val uid: String)
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * Model produced by [[LinearRegression]].
  */
-@AlphaComponent
+@Experimental
 class LinearRegressionModel private[ml] (
     override val uid: String,
     val weights: Vector,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 82437aa8de..ae767a1732 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.ml.regression
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{RandomForestParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -31,12 +31,11 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]] learning algorithm for regression.
  * It supports both continuous and categorical features.
  */
-@AlphaComponent
+@Experimental
 final class RandomForestRegressor(override val uid: String)
   extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel]
   with RandomForestParams with TreeRegressorParams {
@@ -89,6 +88,7 @@ final class RandomForestRegressor(override val uid: String)
   }
 }
 
+@Experimental
 object RandomForestRegressor {
   /** Accessor for supported impurity settings: variance */
   final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities
@@ -99,13 +99,12 @@ object RandomForestRegressor {
 }
 
 /**
- * :: AlphaComponent ::
- *
+ * :: Experimental ::
  * [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]] model for regression.
  * It supports both continuous and categorical features.
  * @param _trees  Decision trees in the ensemble.
  */
-@AlphaComponent
+@Experimental
 final class RandomForestRegressionModel private[ml] (
     override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel])
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
index d2dec0c76c..6a84176efb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
@@ -17,14 +17,16 @@
 
 package org.apache.spark.ml.tree
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.tree.model.{InformationGainStats => OldInformationGainStats,
   Node => OldNode, Predict => OldPredict}
 
-
 /**
+ * :: DeveloperApi ::
  * Decision tree node interface.
  */
+@DeveloperApi
 sealed abstract class Node extends Serializable {
 
   // TODO: Add aggregate stats (once available).  This will happen after we move the DecisionTree
@@ -89,10 +91,12 @@ private[ml] object Node {
 }
 
 /**
+ * :: DeveloperApi ::
  * Decision tree leaf node.
  * @param prediction  Prediction this node makes
  * @param impurity  Impurity measure at this node (for training data)
  */
+@DeveloperApi
 final class LeafNode private[ml] (
     override val prediction: Double,
     override val impurity: Double) extends Node {
@@ -118,6 +122,7 @@ final class LeafNode private[ml] (
 }
 
 /**
+ * :: DeveloperApi ::
  * Internal Decision Tree node.
  * @param prediction  Prediction this node would make if it were a leaf node
  * @param impurity  Impurity measure at this node (for training data)
@@ -127,6 +132,7 @@ final class LeafNode private[ml] (
  * @param rightChild  Right-hand child node
  * @param split  Information about the test used to split to the left or right child.
  */
+@DeveloperApi
 final class InternalNode private[ml] (
     override val prediction: Double,
     override val impurity: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
index 90f1d05276..7acdeeee72 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
@@ -17,15 +17,18 @@
 
 package org.apache.spark.ml.tree
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.tree.configuration.{FeatureType => OldFeatureType}
 import org.apache.spark.mllib.tree.model.{Split => OldSplit}
 
 
 /**
+ * :: DeveloperApi ::
  * Interface for a "Split," which specifies a test made at a decision tree node
  * to choose the left or right path.
  */
+@DeveloperApi
 sealed trait Split extends Serializable {
 
   /** Index of feature which this split tests */
@@ -52,12 +55,14 @@ private[tree] object Split {
 }
 
 /**
+ * :: DeveloperApi ::
  * Split which tests a categorical feature.
  * @param featureIndex  Index of the feature to test
  * @param _leftCategories  If the feature value is in this set of categories, then the split goes
  *                         left. Otherwise, it goes right.
  * @param numCategories  Number of categories for this feature.
  */
+@DeveloperApi
 final class CategoricalSplit private[ml] (
     override val featureIndex: Int,
     _leftCategories: Array[Double],
@@ -125,11 +130,13 @@ final class CategoricalSplit private[ml] (
 }
 
 /**
+ * :: DeveloperApi ::
  * Split which tests a continuous feature.
  * @param featureIndex  Index of the feature to test
  * @param threshold  If the feature value is <= this threshold, then the split goes left.
  *                    Otherwise, it goes right.
  */
+@DeveloperApi
 final class ContinuousSplit private[ml] (override val featureIndex: Int, val threshold: Double)
   extends Split {
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index 816fcedf2e..a0c5238d96 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.ml.tree
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed}
@@ -26,12 +25,10 @@ import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldG
 import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
 
 /**
- * :: DeveloperApi ::
  * Parameters for Decision Tree-based algorithms.
  *
  * Note: Marked as private and DeveloperApi since this may be made public in the future.
  */
-@DeveloperApi
 private[ml] trait DecisionTreeParams extends PredictorParams {
 
   /**
@@ -265,12 +262,10 @@ private[ml] object TreeRegressorParams {
 }
 
 /**
- * :: DeveloperApi ::
  * Parameters for Decision Tree-based ensemble algorithms.
  *
  * Note: Marked as private and DeveloperApi since this may be made public in the future.
  */
-@DeveloperApi
 private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed {
 
   /**
@@ -307,12 +302,10 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed {
 }
 
 /**
- * :: DeveloperApi ::
  * Parameters for Random Forest algorithms.
  *
  * Note: Marked as private and DeveloperApi since this may be made public in the future.
  */
-@DeveloperApi
 private[ml] trait RandomForestParams extends TreeEnsembleParams {
 
   /**
@@ -377,12 +370,10 @@ private[ml] object RandomForestParams {
 }
 
 /**
- * :: DeveloperApi ::
  * Parameters for Gradient-Boosted Tree algorithms.
  *
  * Note: Marked as private and DeveloperApi since this may be made public in the future.
  */
-@DeveloperApi
 private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index e21ff94a20..2e5a629561 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.tuning
 import com.github.fommil.netlib.F2jBLAS
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml._
 import org.apache.spark.ml.evaluation.Evaluator
 import org.apache.spark.ml.param._
@@ -79,10 +79,10 @@ private[ml] trait CrossValidatorParams extends Params {
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * K-fold cross validation.
  */
-@AlphaComponent
+@Experimental
 class CrossValidator(override val uid: String) extends Estimator[CrossValidatorModel]
   with CrossValidatorParams with Logging {
 
@@ -150,10 +150,10 @@ class CrossValidator(override val uid: String) extends Estimator[CrossValidatorM
 }
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Model from k-fold cross validation.
  */
-@AlphaComponent
+@Experimental
 class CrossValidatorModel private[ml] (
     override val uid: String,
     val bestModel: Model[_])
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
index dafe73d82c..98a8f0330c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
@@ -20,14 +20,14 @@ package org.apache.spark.ml.tuning
 import scala.annotation.varargs
 import scala.collection.mutable
 
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param._
 
 /**
- * :: AlphaComponent ::
+ * :: Experimental ::
  * Builder for a param grid used in grid search-based model selection.
  */
-@AlphaComponent
+@Experimental
 class ParamGridBuilder {
 
   private val paramGrid = mutable.Map.empty[Param[_], Iterable[_]]