diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 8e2b2396be14d..a615ca2cde94a 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -1617,6 +1617,8 @@ class GBTClassifier(JavaProbabilisticClassifier, _GBTClassifierParams,
     ...     leafCol="leafId")
     >>> gbt.setMaxIter(5)
     GBTClassifier...
+    >>> gbt.setMinWeightFractionPerNode(0.049)
+    GBTClassifier...
     >>> gbt.getMaxIter()
     5
     >>> gbt.getFeatureSubsetStrategy()
@@ -1684,14 +1686,15 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="logistic",
                  maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, impurity="variance",
                  featureSubsetStrategy="all", validationTol=0.01, validationIndicatorCol=None,
-                 leafCol="", minWeightFractionPerNode=0.0):
+                 leafCol="", minWeightFractionPerNode=0.0, weightCol=None):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
                  lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, \
                  impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
-                 validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
+                 validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, \
+                 weightCol=None)
         """
         super(GBTClassifier, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -1711,14 +1714,16 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                   lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0,
                   impurity="variance", featureSubsetStrategy="all", validationTol=0.01,
-                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0):
+                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
+                  weightCol=None):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
                   lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, \
                   impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
-                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
+                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, \
+                  weightCol=None)
         Sets params for Gradient Boosted Tree Classification.
         """
         kwargs = self._input_kwargs
@@ -1826,6 +1831,20 @@ def setStepSize(self, value):
         """
         return self._set(stepSize=value)
 
+    @since("3.0.0")
+    def setWeightCol(self, value):
+        """
+        Sets the value of :py:attr:`weightCol`.
+        """
+        return self._set(weightCol=value)
+
+    @since("3.0.0")
+    def setMinWeightFractionPerNode(self, value):
+        """
+        Sets the value of :py:attr:`minWeightFractionPerNode`.
+        """
+        return self._set(minWeightFractionPerNode=value)
+
 
 class GBTClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel,
                              _GBTClassifierParams, JavaMLWritable, JavaMLReadable):
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index fdb04bb5115c5..0ea906d2688a1 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -1226,6 +1226,8 @@ class GBTRegressor(JavaPredictor, _GBTRegressorParams, JavaMLWritable, JavaMLRea
     >>> gbt = GBTRegressor(maxDepth=2, seed=42, leafCol="leafId")
     >>> gbt.setMaxIter(5)
     GBTRegressor...
+    >>> gbt.setMinWeightFractionPerNode(0.049)
+    GBTRegressor...
     >>> gbt.getMaxIter()
     5
     >>> print(gbt.getImpurity())
@@ -1285,14 +1287,16 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None,
                  impurity="variance", featureSubsetStrategy="all", validationTol=0.01,
-                 validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0):
+                 validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
+                 weightCol=None):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, \
                  impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
-                 validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
+                 validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
+                 weightCol=None)
         """
         super(GBTRegressor, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", self.uid)
@@ -1311,14 +1315,16 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
                   checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None,
                   impuriy="variance", featureSubsetStrategy="all", validationTol=0.01,
-                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0):
+                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
+                  weightCol=None):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                   maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
                   checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None, \
                   impurity="variance", featureSubsetStrategy="all", validationTol=0.01, \
-                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0)
+                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0, \
+                  weightCol=None)
         Sets params for Gradient Boosted Tree Regression.
         """
         kwargs = self._input_kwargs
@@ -1432,6 +1438,20 @@ def setStepSize(self, value):
         """
         return self._set(stepSize=value)
 
+    @since("3.0.0")
+    def setWeightCol(self, value):
+        """
+        Sets the value of :py:attr:`weightCol`.
+        """
+        return self._set(weightCol=value)
+
+    @since("3.0.0")
+    def setMinWeightFractionPerNode(self, value):
+        """
+        Sets the value of :py:attr:`minWeightFractionPerNode`.
+        """
+        return self._set(minWeightFractionPerNode=value)
+
 
 class GBTRegressionModel(_TreeEnsembleModel, _GBTRegressorParams, JavaMLWritable, JavaMLReadable):
     """