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 dff84224a0e31..ebcee86acedfd 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
@@ -60,72 +60,75 @@ class MulticlassMetrics(predictionsAndLabels: RDD[(Double, Double)]) extends Log
    * @param label the label.
    * @return F1-measure.
    */
-  def f1Measure(label: Double): Double =
-    2 * precision(label) * recall(label) / (precision(label) + recall(label))
+  def f1Measure(label: Double): Double ={
+    val p = precision(label)
+    val r = recall(label)
+    if((p + r) == 0) 0 else 2 * p * r / (p + r)
+  }
 
   /**
    * Returns micro-averaged Recall
    * (equals to microPrecision and microF1measure for multiclass classifier)
    * @return microRecall.
    */
-  def microRecall: Double =
-    tpByClass.foldLeft(0L){case (sum,(_, tp)) => sum + tp}.toDouble / labelCount.toDouble
+  lazy val microRecall: Double =
+    tpByClass.foldLeft(0L){case (sum,(_, tp)) => sum + tp}.toDouble / labelCount
 
   /**
    * Returns micro-averaged Precision
    * (equals to microPrecision and microF1measure for multiclass classifier)
    * @return microPrecision.
    */
-  def microPrecision: Double = microRecall
+  lazy val microPrecision: Double = microRecall
 
   /**
    * Returns micro-averaged F1-measure
    * (equals to microPrecision and microRecall for multiclass classifier)
    * @return microF1measure.
    */
-  def microF1Measure: Double = microRecall
+  lazy val microF1Measure: Double = microRecall
 
   /**
    * Returns weighted averaged Recall
    * @return weightedRecall.
    */
-  def weightedRecall: Double = labelCountByClass.foldLeft(0.0){case(wRecall, (category, count)) =>
-    wRecall + recall(category) * count.toDouble / labelCount.toDouble}
+  lazy val weightedRecall: Double = labelCountByClass.foldLeft(0.0){case(wRecall, (category, count)) =>
+    wRecall + recall(category) * count.toDouble / labelCount}
 
   /**
    * Returns weighted averaged Precision
    * @return weightedPrecision.
    */
-  def weightedPrecision: Double =
+  lazy val weightedPrecision: Double =
     labelCountByClass.foldLeft(0.0){case(wPrecision, (category, count)) =>
-    wPrecision + precision(category) * count.toDouble / labelCount.toDouble}
+    wPrecision + precision(category) * count.toDouble / labelCount}
 
   /**
    * Returns weighted averaged F1-measure
    * @return weightedF1Measure.
    */
-  def weightedF1Measure: Double =
+  lazy val weightedF1Measure: Double =
     labelCountByClass.foldLeft(0.0){case(wF1measure, (category, count)) =>
-    wF1measure + f1Measure(category) * count.toDouble / labelCount.toDouble}
+    wF1measure + f1Measure(category) * count.toDouble / labelCount}
 
   /**
    * Returns map with Precisions for individual classes
    * @return precisionPerClass.
    */
-  def precisionPerClass =
+  lazy val precisionPerClass =
     labelCountByClass.map{case (category, _) => (category, precision(category))}.toMap
 
   /**
    * Returns map with Recalls for individual classes
    * @return recallPerClass.
    */
-  def recallPerClass =
+  lazy val recallPerClass =
     labelCountByClass.map{case (category, _) => (category, recall(category))}.toMap
 
   /**
    * Returns map with F1-measures for individual classes
    * @return f1MeasurePerClass.
    */
-  def f1MeasurePerClass =
+  lazy val f1MeasurePerClass =
     labelCountByClass.map{case (category, _) => (category, f1Measure(category))}.toMap
 }