[SPARK-10691][ML] Make LogisticRegressionModel, LinearRegressionModel evaluate() public

## What changes were proposed in this pull request?

Made evaluate method public.  Fixed LogisticRegressionModel evaluate to handle case when probabilityCol is not specified.

## How was this patch tested?

There were already unit tests for these methods.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #11928 from jkbradley/public-evaluate.

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

@@ -539,13 +539,15 @@ class LogisticRegressionModel private[spark] (
   def hasSummary: Boolean = trainingSummary.isDefined
 
   /**
-   * Evaluates the model on a testset.
+   * Evaluates the model on a test dataset.
    * @param dataset Test dataset to evaluate model on.
    */
-  // TODO: decide on a good name before exposing to public API
-  private[classification] def evaluate(dataset: DataFrame): LogisticRegressionSummary = {
-    new BinaryLogisticRegressionSummary(
-      this.transform(dataset), $(probabilityCol), $(labelCol), $(featuresCol))
+  @Since("2.0.0")
+  def evaluate(dataset: DataFrame): LogisticRegressionSummary = {
+    // Handle possible missing or invalid prediction columns
+    val (summaryModel, probabilityColName) = findSummaryModelAndProbabilityCol()
+    new BinaryLogisticRegressionSummary(summaryModel.transform(dataset),
+      probabilityColName, $(labelCol), $(featuresCol))
   }
 
   /**

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -412,15 +412,15 @@ class LinearRegressionModel private[ml] (
   def hasSummary: Boolean = trainingSummary.isDefined
 
   /**
-   * Evaluates the model on a testset.
+   * Evaluates the model on a test dataset.
    * @param dataset Test dataset to evaluate model on.
    */
-  // TODO: decide on a good name before exposing to public API
-  private[regression] def evaluate(dataset: DataFrame): LinearRegressionSummary = {
+  @Since("2.0.0")
+  def evaluate(dataset: DataFrame): LinearRegressionSummary = {
     // Handle possible missing or invalid prediction columns
     val (summaryModel, predictionColName) = findSummaryModelAndPredictionCol()
     new LinearRegressionSummary(summaryModel.transform(dataset), predictionColName,
-      $(labelCol), this, Array(0D))
+      $(labelCol), summaryModel, Array(0D))
   }
 
   /**