diff --git a/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala b/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala
index e7f7a8e07d7f2..3e32f746e9cd9 100644
--- a/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala
+++ b/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala
@@ -55,7 +55,9 @@ class MultivariateGaussian @Since("2.0.0") (
    *    rootSigmaInv = D^(-1/2)^ * U.t, where sigma = U * D * U.t
    *    u = log((2*pi)^(-k/2)^ * det(sigma)^(-1/2)^)
    */
-  @transient private lazy val (rootSigmaInv: BDM[Double], u: Double) = calculateCovarianceConstants
+  @transient private lazy val tuple = calculateCovarianceConstants
+  @transient private lazy val rootSigmaInv = tuple._1
+  @transient private lazy val u = tuple._2
 
   /**
    * Returns density of this multivariate Gaussian at given point, x
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 94681ae9ef796..5459a0fab9135 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -405,18 +405,26 @@ class NaiveBayesModel private[ml] (
    * This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra
    * application of this condition (in predict function).
    */
-  @transient private lazy val (thetaMinusNegTheta, piMinusThetaSum) = $(modelType) match {
+  @transient private lazy val thetaMinusNegTheta = $(modelType) match {
+    case Bernoulli =>
+      theta.map(value => value - math.log1p(-math.exp(value)))
+    case _ =>
+      // This should never happen.
+      throw new IllegalArgumentException(s"Invalid modelType: ${$(modelType)}. " +
+        "Variables thetaMinusNegTheta should only be precomputed in Bernoulli NB.")
+  }
+
+  @transient private lazy val piMinusThetaSum = $(modelType) match {
     case Bernoulli =>
-      val thetaMinusNegTheta = theta.map(value => value - math.log1p(-math.exp(value)))
       val negTheta = theta.map(value => math.log1p(-math.exp(value)))
       val ones = new DenseVector(Array.fill(theta.numCols)(1.0))
       val piMinusThetaSum = pi.toDense.copy
       BLAS.gemv(1.0, negTheta, ones, 1.0, piMinusThetaSum)
-      (thetaMinusNegTheta, piMinusThetaSum)
+      piMinusThetaSum
     case _ =>
       // This should never happen.
       throw new IllegalArgumentException(s"Invalid modelType: ${$(modelType)}. " +
-        "Variables thetaMinusNegTheta and negThetaSum should only be precomputed in Bernoulli NB.")
+        "Variables piMinusThetaSum should only be precomputed in Bernoulli NB.")
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
index 9a746dcf35556..f34c22915ae15 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
@@ -60,7 +60,9 @@ class MultivariateGaussian @Since("1.3.0") (
    *    rootSigmaInv = D^(-1/2)^ * U.t, where sigma = U * D * U.t
    *    u = log((2*pi)^(-k/2)^ * det(sigma)^(-1/2)^)
    */
-  @transient private lazy val (rootSigmaInv: DBM[Double], u: Double) = calculateCovarianceConstants
+  @transient private lazy val tuple = calculateCovarianceConstants
+  @transient private lazy val rootSigmaInv = tuple._1
+  @transient private lazy val u = tuple._2
 
   /**
    * Returns density of this multivariate Gaussian at given point, x