diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala
index e469a7ac9bc85..34b447584e521 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala
@@ -48,12 +48,13 @@ private[mllib] class BinaryClassificationPMMLModelExport(
        val regressionTableYES = new RegressionTable(model.intercept).withTargetCategory("1")
        var interceptNO = threshold
        if (RegressionNormalizationMethodType.LOGIT == normalizationMethod) {
-         if (threshold <= 0)
-           interceptNO = -1000
-         else if (threshold >= 1)
-           interceptNO = 1000
-         else
-           interceptNO = -math.log(1/threshold -1)
+         if (threshold <= 0) {
+           interceptNO = Double.MinValue
+         } else if (threshold >= 1) {
+           interceptNO = Double.MaxValue
+         } else {
+           interceptNO = -math.log(1 / threshold - 1)
+         }
        }
        val regressionTableNO = new RegressionTable(interceptNO).withTargetCategory("0")
        val regressionModel = new RegressionModel()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala
index 965e2785c3acc..c16e83d6a067d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala
@@ -47,13 +47,14 @@ private[mllib] object PMMLModelExportFactory {
           svm, "linear SVM", RegressionNormalizationMethodType.NONE, 
           svm.getThreshold.getOrElse(0.0))
       case logistic: LogisticRegressionModel =>
-        if (logistic.numClasses == 2)
+        if (logistic.numClasses == 2) {
           new BinaryClassificationPMMLModelExport(
             logistic, "logistic regression", RegressionNormalizationMethodType.LOGIT,
             logistic.getThreshold.getOrElse(0.5))
-        else
+        } else {
           throw new IllegalArgumentException(
             "PMML Export not supported for Multinomial Logistic Regression")
+        }
       case _ =>
         throw new IllegalArgumentException(
           "PMML Export not supported for model: " + model.getClass.getName)