diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index abe2d1febfdf..a5ed4a38a886 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -341,11 +341,12 @@ class GBTClassificationModel private[ml](
    * The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
    * (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
    * and follows the implementation from scikit-learn.
-
+   *
    * See `DecisionTreeClassificationModel.featureImportances`
    */
   @Since("2.0.0")
-  lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
+  lazy val featureImportances: Vector =
+    TreeEnsembleModel.featureImportances(trees, numFeatures, perTreeNormalization = false)
 
   /** Raw prediction for the positive class. */
   private def margin(features: Vector): Double = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 9a5b7d59e9ae..9f0f567a5b53 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -285,7 +285,8 @@ class GBTRegressionModel private[ml](
    * @see `DecisionTreeRegressionModel.featureImportances`
    */
   @Since("2.0.0")
-  lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
+  lazy val featureImportances: Vector =
+    TreeEnsembleModel.featureImportances(trees, numFeatures, perTreeNormalization = false)
 
   /** (private[ml]) Convert to a model in the old API */
   private[ml] def toOld: OldGBTModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
index 51d5d5c58c57..e95c55f6048f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
@@ -135,7 +135,7 @@ private[ml] object TreeEnsembleModel {
    *  - Average over trees:
    *     - importance(feature j) = sum (over nodes which split on feature j) of the gain,
    *       where gain is scaled by the number of instances passing through node
-   *     - Normalize importances for tree to sum to 1.
+   *     - Normalize importances for tree to sum to 1 (only if `perTreeNormalization` is `true`).
    *  - Normalize feature importance vector to sum to 1.
    *
    *  References:
@@ -145,9 +145,15 @@ private[ml] object TreeEnsembleModel {
    * @param numFeatures  Number of features in model (even if not all are explicitly used by
    *                     the model).
    *                     If -1, then numFeatures is set based on the max feature index in all trees.
+   * @param perTreeNormalization By default this is set to `true` and it means that the importances
+   *                             of each tree are normalized before being summed. If set to `false`,
+   *                             the normalization is skipped.
    * @return  Feature importance values, of length numFeatures.
    */
-  def featureImportances[M <: DecisionTreeModel](trees: Array[M], numFeatures: Int): Vector = {
+  def featureImportances[M <: DecisionTreeModel](
+      trees: Array[M],
+      numFeatures: Int,
+      perTreeNormalization: Boolean = true): Vector = {
     val totalImportances = new OpenHashMap[Int, Double]()
     trees.foreach { tree =>
       // Aggregate feature importance vector for this tree
@@ -155,10 +161,19 @@ private[ml] object TreeEnsembleModel {
       computeFeatureImportance(tree.rootNode, importances)
       // Normalize importance vector for this tree, and add it to total.
       // TODO: In the future, also support normalizing by tree.rootNode.impurityStats.count?
-      val treeNorm = importances.map(_._2).sum
+      val treeNorm = if (perTreeNormalization) {
+        importances.map(_._2).sum
+      } else {
+        // We won't use it
+        Double.NaN
+      }
       if (treeNorm != 0) {
         importances.foreach { case (idx, impt) =>
-          val normImpt = impt / treeNorm
+          val normImpt = if (perTreeNormalization) {
+            impt / treeNorm
+          } else {
+            impt
+          }
           totalImportances.changeValue(idx, normImpt, _ + normImpt)
         }
       }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index cedbaf1858ef..cd59900c521c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -363,7 +363,8 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
     val gbtWithFeatureSubset = gbt.setFeatureSubsetStrategy("1")
     val importanceFeatures = gbtWithFeatureSubset.fit(df).featureImportances
     val mostIF = importanceFeatures.argmax
-    assert(mostImportantFeature !== mostIF)
+    assert(mostIF === 1)
+    assert(importances(mostImportantFeature) !== importanceFeatures(mostIF))
   }
 
   test("model evaluateEachIteration") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index b145c7a3dc95..46fa3767efdc 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -200,7 +200,8 @@ class GBTRegressorSuite extends MLTest with DefaultReadWriteTest {
     val gbtWithFeatureSubset = gbt.setFeatureSubsetStrategy("1")
     val importanceFeatures = gbtWithFeatureSubset.fit(df).featureImportances
     val mostIF = importanceFeatures.argmax
-    assert(mostImportantFeature !== mostIF)
+    assert(mostIF === 1)
+    assert(importances(mostImportantFeature) !== importanceFeatures(mostIF))
   }
 
   test("model evaluateEachIteration") {