[SPARK-15457][MLLIB][ML] Eliminate some warnings from MLlib about deprecations

## What changes were proposed in this pull request?

Several classes and methods have been deprecated and are creating lots of build warnings in branch-2.0. This issue is to identify and fix those items:
* WithSGD classes: Change to make class not deprecated, object deprecated, and public class constructor deprecated. Any public use will require a deprecated API. We need to keep a non-deprecated private API since we cannot eliminate certain uses: Python API, streaming algs, and examples.
  * Use in PythonMLlibAPI: Change to using private constructors
  * Streaming algs: No warnings after we un-deprecate the classes
  * Examples: Deprecate or change ones which use deprecated APIs
* MulticlassMetrics fields (precision, etc.)
* LinearRegressionSummary.model field

## How was this patch tested?

Existing tests.  Checked for warnings manually.

Author: Sean Owen <sowen@cloudera.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #13314 from jkbradley/warning-cleanups.

18 files changed, 63 insertions, 62 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java
index 9d8e4a90db..7fc371ec0f 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java
@@ -65,8 +65,8 @@ public class JavaLogisticRegressionWithLBFGSExample {
 
     // Get evaluation metrics.
     MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd());
-    double precision = metrics.precision();
-    System.out.println("Precision = " + precision);
+    double accuracy = metrics.accuracy();
+    System.out.println("Accuracy = " + accuracy);
 
     // Save and load model
     model.save(sc, "target/tmp/javaLogisticRegressionWithLBFGSModel");
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java
index 5247c9c748..e84a3a712d 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java
@@ -68,9 +68,7 @@ public class JavaMulticlassClassificationMetricsExample {
     System.out.println("Confusion matrix: \n" + confusion);
 
     // Overall statistics
-    System.out.println("Precision = " + metrics.precision());
-    System.out.println("Recall = " + metrics.recall());
-    System.out.println("F1 Score = " + metrics.fMeasure());
+    System.out.println("Accuracy = " + metrics.accuracy());
 
     // Stats by labels
     for (int i = 0; i < metrics.labels().length; i++) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
index 5e51dbad76..de4474555d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
@@ -321,7 +321,7 @@ object DecisionTreeExample {
       case None => throw new RuntimeException(
         "Unknown failure when indexing labels for classification.")
     }
-    val accuracy = new MulticlassMetrics(predictions.zip(labels)).precision
+    val accuracy = new MulticlassMetrics(predictions.zip(labels)).accuracy
     println(s"  Accuracy ($numClasses classes): $accuracy")
   }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index ee811d3aa1..a85aa2cac9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -295,11 +295,10 @@ object DecisionTreeRunner {
       }
       if (params.algo == Classification) {
         val trainAccuracy =
-          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
-            .precision
+          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Train accuracy = $trainAccuracy")
         val testAccuracy =
-          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
+          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Test accuracy = $testAccuracy")
       }
       if (params.algo == Regression) {
@@ -322,11 +321,10 @@ object DecisionTreeRunner {
           println(model) // Print model summary.
         }
         val trainAccuracy =
-          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
-            .precision
+          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Train accuracy = $trainAccuracy")
         val testAccuracy =
-          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
+          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Test accuracy = $testAccuracy")
       }
       if (params.algo == Regression) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
index b0144ef533..90e4687c1f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
@@ -120,11 +120,10 @@ object GradientBoostedTreesRunner {
         println(model) // Print model summary.
       }
       val trainAccuracy =
-        new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
-          .precision
+        new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label))).accuracy
       println(s"Train accuracy = $trainAccuracy")
       val testAccuracy =
-        new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
+        new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).accuracy
       println(s"Test accuracy = $testAccuracy")
     } else if (params.algo == "Regression") {
       val startTime = System.nanoTime()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
index f87611f5d4..a70203028c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
@@ -34,6 +34,7 @@ import org.apache.spark.mllib.util.MLUtils
  * A synthetic dataset can be found at `data/mllib/sample_linear_regression_data.txt`.
  * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */
+@deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
 object LinearRegression {
 
   object RegType extends Enumeration {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala
index 669868787e..d399618094 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala
@@ -26,6 +26,7 @@ import org.apache.spark.mllib.regression.LinearRegressionModel
 import org.apache.spark.mllib.regression.LinearRegressionWithSGD
 // $example off$
 
+@deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
 object LinearRegressionWithSGDExample {
 
   def main(args: Array[String]): Unit = {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala
index 632a2d537e..31ba740ad4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala
@@ -54,8 +54,8 @@ object LogisticRegressionWithLBFGSExample {
 
     // Get evaluation metrics.
     val metrics = new MulticlassMetrics(predictionAndLabels)
-    val precision = metrics.precision
-    println("Precision = " + precision)
+    val accuracy = metrics.accuracy
+    println(s"Accuracy = $accuracy")
 
     // Save and load model
     model.save(sc, "target/tmp/scalaLogisticRegressionWithLBFGSModel")
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala
index 4f925ede24..12394c867e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala
@@ -59,13 +59,9 @@ object MulticlassMetricsExample {
     println(metrics.confusionMatrix)
 
     // Overall Statistics
-    val precision = metrics.precision
-    val recall = metrics.recall // same as true positive rate
-    val f1Score = metrics.fMeasure
+    val accuracy = metrics.accuracy
     println("Summary Statistics")
-    println(s"Precision = $precision")
-    println(s"Recall = $recall")
-    println(s"F1 Score = $f1Score")
+    println(s"Accuracy = $accuracy")
 
     // Precision by label
     val labels = metrics.labels
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
index f7a8136953..eb36697d94 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
@@ -26,6 +26,7 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
 // $example off$
 
+@deprecated("Deprecated since LinearRegressionWithSGD is deprecated.  Use ml.feature.PCA", "2.0.0")
 object PCAExample {
 
   def main(args: Array[String]): Unit = {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
index abeaaa00b5..76cfb804e1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
@@ -25,6 +25,8 @@ import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
 // $example off$
 import org.apache.spark.sql.SparkSession
 
+@deprecated("Use ml.regression.LinearRegression and the resulting model summary for metrics",
+  "2.0.0")
 object RegressionMetricsExample {
   def main(args: Array[String]): Unit = {
     val spark = SparkSession
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
index 390e9b6444..0b84e0a3fa 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
@@ -82,8 +82,8 @@ class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") overrid
     val metrics = new MulticlassMetrics(predictionAndLabels)
     val metric = $(metricName) match {
       case "f1" => metrics.weightedFMeasure
-      case "precision" => metrics.precision
-      case "recall" => metrics.recall
+      case "precision" => metrics.accuracy
+      case "recall" => metrics.accuracy
       case "weightedPrecision" => metrics.weightedPrecision
       case "weightedRecall" => metrics.weightedRecall
       case "accuracy" => metrics.accuracy
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 ff1038cbf1..37552194c5 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
@@ -558,16 +558,18 @@ class LinearRegressionSummary private[regression] (
     val predictionCol: String,
     val labelCol: String,
     val featuresCol: String,
-    @deprecated("The model field is deprecated and will be removed in 2.1.0.", "2.0.0")
-    val model: LinearRegressionModel,
+    private val privateModel: LinearRegressionModel,
     private val diagInvAtWA: Array[Double]) extends Serializable {
 
+  @deprecated("The model field is deprecated and will be removed in 2.1.0.", "2.0.0")
+  val model: LinearRegressionModel = privateModel
+
   @transient private val metrics = new RegressionMetrics(
     predictions
       .select(col(predictionCol), col(labelCol).cast(DoubleType))
       .rdd
       .map { case Row(pred: Double, label: Double) => (pred, label) },
-    !model.getFitIntercept)
+    !privateModel.getFitIntercept)
 
   /**
    * Returns the explained variance regression score.
@@ -631,10 +633,10 @@ class LinearRegressionSummary private[regression] (
   lazy val numInstances: Long = predictions.count()
 
   /** Degrees of freedom */
-  private val degreesOfFreedom: Long = if (model.getFitIntercept) {
-    numInstances - model.coefficients.size - 1
+  private val degreesOfFreedom: Long = if (privateModel.getFitIntercept) {
+    numInstances - privateModel.coefficients.size - 1
   } else {
-    numInstances - model.coefficients.size
+    numInstances - privateModel.coefficients.size
   }
 
   /**
@@ -642,13 +644,15 @@ class LinearRegressionSummary private[regression] (
    * the square root of the instance weights.
    */
   lazy val devianceResiduals: Array[Double] = {
-    val weighted = if (!model.isDefined(model.weightCol) || model.getWeightCol.isEmpty) {
-      lit(1.0)
-    } else {
-      sqrt(col(model.getWeightCol))
-    }
-    val dr = predictions.select(col(model.getLabelCol).minus(col(model.getPredictionCol))
-      .multiply(weighted).as("weightedResiduals"))
+    val weighted =
+      if (!privateModel.isDefined(privateModel.weightCol) || privateModel.getWeightCol.isEmpty) {
+        lit(1.0)
+      } else {
+        sqrt(col(privateModel.getWeightCol))
+      }
+    val dr = predictions
+      .select(col(privateModel.getLabelCol).minus(col(privateModel.getPredictionCol))
+        .multiply(weighted).as("weightedResiduals"))
       .select(min(col("weightedResiduals")).as("min"), max(col("weightedResiduals")).as("max"))
       .first()
     Array(dr.getDouble(0), dr.getDouble(1))
@@ -668,14 +672,15 @@ class LinearRegressionSummary private[regression] (
       throw new UnsupportedOperationException(
         "No Std. Error of coefficients available for this LinearRegressionModel")
     } else {
-      val rss = if (!model.isDefined(model.weightCol) || model.getWeightCol.isEmpty) {
-        meanSquaredError * numInstances
-      } else {
-        val t = udf { (pred: Double, label: Double, weight: Double) =>
-          math.pow(label - pred, 2.0) * weight }
-        predictions.select(t(col(model.getPredictionCol), col(model.getLabelCol),
-          col(model.getWeightCol)).as("wse")).agg(sum(col("wse"))).first().getDouble(0)
-      }
+      val rss =
+        if (!privateModel.isDefined(privateModel.weightCol) || privateModel.getWeightCol.isEmpty) {
+          meanSquaredError * numInstances
+        } else {
+          val t = udf { (pred: Double, label: Double, weight: Double) =>
+            math.pow(label - pred, 2.0) * weight }
+          predictions.select(t(col(privateModel.getPredictionCol), col(privateModel.getLabelCol),
+            col(privateModel.getWeightCol)).as("wse")).agg(sum(col("wse"))).first().getDouble(0)
+        }
       val sigma2 = rss / degreesOfFreedom
       diagInvAtWA.map(_ * sigma2).map(math.sqrt)
     }
@@ -695,10 +700,10 @@ class LinearRegressionSummary private[regression] (
       throw new UnsupportedOperationException(
         "No t-statistic available for this LinearRegressionModel")
     } else {
-      val estimate = if (model.getFitIntercept) {
-        Array.concat(model.coefficients.toArray, Array(model.intercept))
+      val estimate = if (privateModel.getFitIntercept) {
+        Array.concat(privateModel.coefficients.toArray, Array(privateModel.intercept))
       } else {
-        model.coefficients.toArray
+        privateModel.coefficients.toArray
       }
       estimate.zip(coefficientStandardErrors).map { x => x._1 / x._2 }
     }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 90d3827531..667290ece3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -152,7 +152,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       intercept: Boolean,
       validateData: Boolean,
       convergenceTol: Double): JList[Object] = {
-    val lrAlg = new LinearRegressionWithSGD()
+    val lrAlg = new LinearRegressionWithSGD(1.0, 100, 0.0, 1.0)
     lrAlg.setIntercept(intercept)
       .setValidateData(validateData)
     lrAlg.optimizer
@@ -181,7 +181,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       intercept: Boolean,
       validateData: Boolean,
       convergenceTol: Double): JList[Object] = {
-    val lassoAlg = new LassoWithSGD()
+    val lassoAlg = new LassoWithSGD(1.0, 100, 0.01, 1.0)
     lassoAlg.setIntercept(intercept)
       .setValidateData(validateData)
     lassoAlg.optimizer
@@ -209,7 +209,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       intercept: Boolean,
       validateData: Boolean,
       convergenceTol: Double): JList[Object] = {
-    val ridgeAlg = new RidgeRegressionWithSGD()
+    val ridgeAlg = new RidgeRegressionWithSGD(1.0, 100, 0.01, 1.0)
     ridgeAlg.setIntercept(intercept)
       .setValidateData(validateData)
     ridgeAlg.optimizer
@@ -268,7 +268,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       intercept: Boolean,
       validateData: Boolean,
       convergenceTol: Double): JList[Object] = {
-    val LogRegAlg = new LogisticRegressionWithSGD()
+    val LogRegAlg = new LogisticRegressionWithSGD(1.0, 100, 0.01, 1.0)
     LogRegAlg.setIntercept(intercept)
       .setValidateData(validateData)
     LogRegAlg.optimizer
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 f3c52f61a3..adbcdd302a 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
@@ -200,13 +200,12 @@ object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
 /**
  * Train a classification model for Binary Logistic Regression
  * using Stochastic Gradient Descent. By default L2 regularization is used,
- * which can be changed via [[LogisticRegressionWithSGD.optimizer]].
+ * which can be changed via `LogisticRegressionWithSGD.optimizer`.
  * NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1}
  * for k classes multi-label classification problem.
  * Using [[LogisticRegressionWithLBFGS]] is recommended over this.
  */
 @Since("0.8.0")
-@deprecated("Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS", "2.0.0")
 class LogisticRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -229,6 +228,7 @@ class LogisticRegressionWithSGD private[mllib] (
    * numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.
    */
   @Since("0.8.0")
+  @deprecated("Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS", "2.0.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index ef8c80f0cb..e14bddf97d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -85,9 +85,7 @@ object LassoModel extends Loader[LassoModel] {
  * See also the documentation for the precise formulation.
  */
 @Since("0.8.0")
-@deprecated("Use ml.regression.LinearRegression with elasticNetParam = 1.0. Note the default " +
-  "regParam is 0.01 for LassoWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
-class LassoWithSGD private (
+class LassoWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
     private var regParam: Double,
@@ -108,6 +106,8 @@ class LassoWithSGD private (
    * regParam: 0.01, miniBatchFraction: 1.0}.
    */
   @Since("0.8.0")
+  @deprecated("Use ml.regression.LinearRegression with elasticNetParam = 1.0. Note the default " +
+    "regParam is 0.01 for LassoWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 9e9d98bc5e..2ceac4b8cc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -86,7 +86,6 @@ object LinearRegressionModel extends Loader[LinearRegressionModel] {
  * See also the documentation for the precise formulation.
  */
 @Since("0.8.0")
-@deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
 class LinearRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -108,6 +107,7 @@ class LinearRegressionWithSGD private[mllib] (
    * numIterations: 100, miniBatchFraction: 1.0}.
    */
   @Since("0.8.0")
+  @deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
   def this() = this(1.0, 100, 0.0, 1.0)
 
   override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 512fb9a712..301f02fd98 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -86,9 +86,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
  * See also the documentation for the precise formulation.
  */
 @Since("0.8.0")
-@deprecated("Use ml.regression.LinearRegression with elasticNetParam = 0.0. Note the default " +
-  "regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
-class RidgeRegressionWithSGD private (
+class RidgeRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
     private var regParam: Double,
@@ -109,6 +107,8 @@ class RidgeRegressionWithSGD private (
    * regParam: 0.01, miniBatchFraction: 1.0}.
    */
   @Since("0.8.0")
+  @deprecated("Use ml.regression.LinearRegression with elasticNetParam = 0.0. Note the default " +
+    "regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {