diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index 76fe96a5938c0..dff84224a0e31 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -23,18 +23,21 @@ import org.apache.spark.SparkContext._
 
 /**
  * Evaluator for multiclass classification.
+ * NB: type Double both for prediction and label is retained
+ * for compatibility with model.predict that returns Double
+ * and MLUtils.loadLibSVMFile that loads class labels as Double
  *
- * @param scoreAndLabels an RDD of (score, label) pairs.
+ * @param predictionsAndLabels an RDD of (prediction, label) pairs.
  */
-class MulticlassMetrics(scoreAndLabels: RDD[(Double, Double)]) extends Logging {
+class MulticlassMetrics(predictionsAndLabels: RDD[(Double, Double)]) extends Logging {
 
   /* class = category; label = instance of class; prediction = instance of class */
 
-  private lazy val labelCountByClass = scoreAndLabels.values.countByValue()
+  private lazy val labelCountByClass = predictionsAndLabels.values.countByValue()
   private lazy val labelCount = labelCountByClass.foldLeft(0L){case(sum, (_, count)) => sum + count}
-  private lazy val tpByClass = scoreAndLabels.map{ case (prediction, label) =>
+  private lazy val tpByClass = predictionsAndLabels.map{ case (prediction, label) =>
     (label, if(label == prediction) 1 else 0) }.reduceByKey{_ + _}.collectAsMap
-  private lazy val fpByClass = scoreAndLabels.map{ case (prediction, label) =>
+  private lazy val fpByClass = predictionsAndLabels.map{ case (prediction, label) =>
     (prediction, if(prediction != label) 1 else 0) }.reduceByKey{_ + _}.collectAsMap
 
   /**