diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 4d763cbd29d3c..a43ad466a7c80 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -1100,20 +1100,24 @@ class LogisticRegressionModel private[spark] (
 
   private lazy val _intercept = interceptVector(0)
   private lazy val _interceptVector = interceptVector.toDense
-  private var _threshold = Double.NaN
-  private var _rawThreshold = Double.NaN
-
-  updateBinaryThreshold()
+  private lazy val _binaryThresholdArray = {
+    val array = Array(Double.NaN, Double.NaN)
+    updateBinaryThresholds(array)
+    array
+  }
+  private def _threshold: Double = _binaryThresholdArray(0)
+  private def _rawThreshold: Double = _binaryThresholdArray(1)
 
-  private def updateBinaryThreshold(): Unit = {
+  private def updateBinaryThresholds(array: Array[Double]): Unit = {
     if (!isMultinomial) {
-      _threshold = getThreshold
+      val _threshold = getThreshold
+      array(0) = _threshold
       if (_threshold == 0.0) {
-        _rawThreshold = Double.NegativeInfinity
+        array(1) = Double.NegativeInfinity
       } else if (_threshold == 1.0) {
-        _rawThreshold = Double.PositiveInfinity
+        array(1) = Double.PositiveInfinity
       } else {
-        _rawThreshold = math.log(_threshold / (1.0 - _threshold))
+        array(1) = math.log(_threshold / (1.0 - _threshold))
       }
     }
   }
@@ -1121,7 +1125,7 @@ class LogisticRegressionModel private[spark] (
   @Since("1.5.0")
   override def setThreshold(value: Double): this.type = {
     super.setThreshold(value)
-    updateBinaryThreshold()
+    updateBinaryThresholds(_binaryThresholdArray)
     this
   }
 
@@ -1131,7 +1135,7 @@ class LogisticRegressionModel private[spark] (
   @Since("1.5.0")
   override def setThresholds(value: Array[Double]): this.type = {
     super.setThresholds(value)
-    updateBinaryThreshold()
+    updateBinaryThresholds(_binaryThresholdArray)
     this
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 56eadff6df078..51a6ae3c7e49b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -400,10 +400,9 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
   }
 
   test("thresholds prediction") {
-    val blr = new LogisticRegression().setFamily("binomial")
+    val blr = new LogisticRegression().setFamily("binomial").setThreshold(1.0)
     val binaryModel = blr.fit(smallBinaryDataset)
 
-    binaryModel.setThreshold(1.0)
     testTransformer[(Double, Vector)](smallBinaryDataset.toDF(), binaryModel, "prediction") {
       row => assert(row.getDouble(0) === 0.0)
     }