diff --git a/python/interpret-core/interpret/glassbox/_decisiontree.py b/python/interpret-core/interpret/glassbox/_decisiontree.py
index dd82b2ca1..a3d491d58 100644
--- a/python/interpret-core/interpret/glassbox/_decisiontree.py
+++ b/python/interpret-core/interpret/glassbox/_decisiontree.py
@@ -260,12 +260,14 @@ def _model(self):
         # This method should be overriden
         return None
 
-    def fit(self, X, y):
+    def fit(self, X, y, sample_weight=None, check_input=True):
         """Fits model to provided instances.
 
         Args:
             X: Numpy array for training instances.
             y: Numpy array as training labels.
+            sample_weight (optional[np.ndarray]): (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
+            check_input (bool): default=True. Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.
 
         Returns:
             Itself.
@@ -289,7 +291,7 @@ def fit(self, X, y):
         )
 
         model = self._model()
-        model.fit(X, y)
+        model.fit(X, y, sample_weight=sample_weight, check_input=check_input)
 
         unique_val_counts = np.zeros(len(self.feature_names_in_), dtype=np.int64)
         for col_idx in range(len(self.feature_names_in_)):
@@ -571,18 +573,25 @@ def __init__(self, feature_names=None, feature_types=None, max_depth=3, **kwargs
     def _model(self):
         return self.sk_model_
 
-    def fit(self, X, y):
+    def fit(self, X, y, sample_weight=None, check_input=True):
         """Fits model to provided instances.
 
         Args:
             X: Numpy array for training instances.
             y: Numpy array as training labels.
+            sample_weight (optional[np.ndarray]): (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
+            check_input (bool): default=True. Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.
 
         Returns:
             Itself.
         """
         self.sk_model_ = SKRT(max_depth=self.max_depth, **self.kwargs)
-        return super().fit(X, y)
+        return super().fit(
+            X,
+            y,
+            sample_weight=sample_weight,
+            check_input=check_input,
+        )
 
 
 class ClassificationTree(BaseShallowDecisionTree, ClassifierMixin, ExplainerMixin):
@@ -607,18 +616,25 @@ def __init__(self, feature_names=None, feature_types=None, max_depth=3, **kwargs
     def _model(self):
         return self.sk_model_
 
-    def fit(self, X, y):
+    def fit(self, X, y, sample_weight=None, check_input=True):
         """Fits model to provided instances.
 
         Args:
             X: Numpy array for training instances.
             y: Numpy array as training labels.
+            sample_weight (optional[np.ndarray]): (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
+            check_input (bool): default=True. Allow to bypass several input checking. Don't use this parameter unless you know what you're doing.
 
         Returns:
             Itself.
         """
         self.sk_model_ = SKDT(max_depth=self.max_depth, **self.kwargs)
-        return super().fit(X, y)
+        return super().fit(
+            X,
+            y,
+            sample_weight=sample_weight,
+            check_input=check_input,
+        )
 
     def predict_proba(self, X):
         """Probability estimates on provided instances.