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 d99b63a414481..fe511bd904e70 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -84,7 +84,7 @@ class PipelineModel(
    * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input
    * estimator does not exist in the pipeline.
    */
-  def getModel[M](estimator: Estimator[M]): M = {
+  def getModel[M <: Model](estimator: Estimator[M]): M = {
     val matched = transformers.filter {
       case m: Model => m.parent.eq(estimator)
       case _ => false
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 cb67d7c4b2af3..eba1b9c36c7ff 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
@@ -84,7 +84,7 @@ abstract class UnaryTransformer[IN, OUT: TypeTag, SELF <: UnaryTransformer[IN, O
   override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = {
     import dataset.sqlContext._
     val map = this.paramMap ++ paramMap
-    val udf: IN => OUT = this.createTransformFunc(map)
+    val udf = this.createTransformFunc(map)
     dataset.select(Star(None), udf.call(map(inputCol).attr) as map(outputCol))
   }
 }
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 1ab8f2e55c852..b7ce0e644c887 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
@@ -28,11 +28,14 @@ import org.apache.spark.storage.StorageLevel
  * Evaluator for binary classification, which expects two input columns: score and label.
  */
 class BinaryClassificationEvaluator extends Evaluator with Params
-    with HasScoreCol with HasLabelCol with HasMetricName {
-
-  setMetricName("areaUnderROC")
+    with HasScoreCol with HasLabelCol {
 
+  /** param for metric name in evaluation */
+  val metricName: Param[String] = new Param(this, "metricName",
+    "metric name in evaluation (areaUnderROC|areaUnderPR)", Some("areaUnderROC"))
+  def getMetricName: String = get(metricName)
   def setMetricName(value: String): this.type = { set(metricName, value); this }
+
   def setScoreCol(value: String): this.type = { set(scoreCol, value); this }
   def setLabelCol(value: String): this.type = { set(labelCol, value); this }
 
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 17edec7f5b3c3..4999729780b0a 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
@@ -25,7 +25,8 @@ import scala.collection.mutable
 import org.apache.spark.ml.Identifiable
 
 /**
- * A param with self-contained documentation and optionally default value.
+ * 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.
  *
  * @param parent parent object
  * @param name param name
@@ -59,26 +60,31 @@ class Param[T] (
 
 // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ...
 
+/** Specialized version of [[Param[Double]] for Java. */
 class DoubleParam(parent: Params, name: String, doc: String, default: Option[Double] = None)
     extends Param[Double](parent, name, doc, default) {
   override def w(value: Double): ParamPair[Double] = ParamPair(this, value)
 }
 
+/** Specialized version of [[Param[Int]] for Java. */
 class IntParam(parent: Params, name: String, doc: String, default: Option[Int] = None)
     extends Param[Int](parent, name, doc, default) {
   override def w(value: Int): ParamPair[Int] = ParamPair(this, value)
 }
 
+/** Specialized version of [[Param[Float]] for Java. */
 class FloatParam(parent: Params, name: String, doc: String, default: Option[Float] = None)
     extends Param[Float](parent, name, doc, default) {
   override def w(value: Float): ParamPair[Float] = ParamPair(this, value)
 }
 
+/** Specialized version of [[Param[Long]] for Java. */
 class LongParam(parent: Params, name: String, doc: String, default: Option[Long] = None)
     extends Param[Long](parent, name, doc, default) {
   override def w(value: Long): ParamPair[Long] = ParamPair(this, value)
 }
 
+/** Specilized version of [[Param[Boolean]] for Java. */
 class BooleanParam(parent: Params, name: String, doc: String, default: Option[Boolean] = None)
     extends Param[Boolean](parent, name, doc, default) {
   override def w(value: Boolean): ParamPair[Boolean] = ParamPair(this, value)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared.scala
index 6656802601878..dad4f3372644e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared.scala
@@ -63,12 +63,6 @@ private[ml] trait HasThreshold extends Params {
   def getThreshold: Double = get(threshold)
 }
 
-private[ml] trait HasMetricName extends Params {
-  /** param for metric name in evaluation */
-  val metricName: Param[String] = new Param(this, "metricName", "metric name in evaluation")
-  def getMetricName: String = get(metricName)
-}
-
 private[ml] trait HasInputCol extends Params {
   /** param for input column name */
   val inputCol: Param[String] = new Param(this, "inputCol", "input column name")