Use a tigher bound in logistic regression unit test's prediction validation.

1 files changed, 4 insertions, 3 deletions

diff --git a/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
index d3fe58a382..8664263935 100644
--- a/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -21,11 +21,12 @@ import scala.util.Random
 
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
+import org.scalatest.matchers.ShouldMatchers
 
 import spark.SparkContext
 
 
-class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
+class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll with ShouldMatchers {
   val sc = new SparkContext("local", "test")
 
   override def afterAll() {
@@ -64,8 +65,8 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     val numOffPredictions = predictions.zip(input).filter { case (prediction, (expected, _)) =>
       (prediction != expected)
     }.size
-    // At least 80% of the predictions should be on.
-    assert(numOffPredictions < input.length / 5)
+    // At least 83% of the predictions should be on.
+    ((input.length - numOffPredictions).toDouble / input.length) should be > 0.83
   }
 
   // Test if we can correctly learn A, B where Y = logistic(A + B*X)