diff --git a/src/omlt/linear_tree/lt_definition.py b/src/omlt/linear_tree/lt_definition.py
index 6bd26c8f..799bd9d6 100644
--- a/src/omlt/linear_tree/lt_definition.py
+++ b/src/omlt/linear_tree/lt_definition.py
@@ -54,6 +54,7 @@ def __init__(
         self.__model = lt_regressor
         self.__scaling_object = scaling_object
 
+        is_scaled = True
         # Process input bounds to insure scaled input bounds exist for formulations
         if scaled_input_bounds is None:
             if unscaled_input_bounds is not None and scaling_object is not None:
@@ -72,6 +73,7 @@ def __init__(
             # input bounds = unscaled input bounds
             elif unscaled_input_bounds is not None and scaling_object is None:
                 scaled_input_bounds = unscaled_input_bounds
+                is_scaled = False
             elif unscaled_input_bounds is None:
                 raise ValueError(
                     "Input Bounds needed to represent linear trees as MIPs"
@@ -79,6 +81,7 @@ def __init__(
 
         self.__unscaled_input_bounds = unscaled_input_bounds
         self.__scaled_input_bounds = scaled_input_bounds
+        self.__is_scaled = is_scaled
 
         self.__splits, self.__leaves, self.__thresholds = _parse_tree_data(
             lt_regressor, scaled_input_bounds
@@ -97,6 +100,16 @@ def scaled_input_bounds(self):
         """Returns dict containing scaled input bounds"""
         return self.__scaled_input_bounds
 
+    @property
+    def unscaled_input_bounds(self):
+        """Returns dict containing unscaled input bounds"""
+        return self.__unscaled_input_bounds
+
+    @property
+    def is_scaled(self):
+        """Returns bool indicating whether model is scaled"""
+        return self.__is_scaled
+
     @property
     def splits(self):
         """Returns dict containing split information"""
diff --git a/src/omlt/linear_tree/lt_formulation.py b/src/omlt/linear_tree/lt_formulation.py
index 4f83e7f3..6bbb42d7 100644
--- a/src/omlt/linear_tree/lt_formulation.py
+++ b/src/omlt/linear_tree/lt_formulation.py
@@ -229,7 +229,8 @@ def _add_gdp_formulation_to_block(
 
     """
     leaves = model_definition.leaves
-    input_bounds = model_definition.scaled_input_bounds
+    scaled_input_bounds = model_definition.scaled_input_bounds
+    unscaled_input_bounds = model_definition.unscaled_input_bounds
     n_inputs = model_definition.n_inputs
 
     # The set of leaves and the set of features
@@ -242,17 +243,25 @@ def _add_gdp_formulation_to_block(
 
     # Use the input_bounds and the linear models in the leaves to calculate
     # the lower and upper bounds on the output variable. Required for Pyomo.GDP
-    output_bounds = _build_output_bounds(model_definition, input_bounds)
+    scaled_output_bounds = _build_output_bounds(model_definition, scaled_input_bounds)
+    unscaled_output_bounds = _build_output_bounds(
+        model_definition, unscaled_input_bounds
+    )
 
     # Ouptuts are automatically scaled based on whether inputs are scaled
-    block.outputs.setub(output_bounds[1])
-    block.outputs.setlb(output_bounds[0])
-    block.scaled_outputs.setub(output_bounds[1])
-    block.scaled_outputs.setlb(output_bounds[0])
-
-    block.intermediate_output = pe.Var(
-        tree_ids, bounds=(output_bounds[0], output_bounds[1])
-    )
+    block.outputs.setub(unscaled_output_bounds[1])
+    block.outputs.setlb(unscaled_output_bounds[0])
+    block.scaled_outputs.setub(scaled_output_bounds[1])
+    block.scaled_outputs.setlb(scaled_output_bounds[0])
+
+    if model_definition.is_scaled is True:
+        block.intermediate_output = pe.Var(
+            tree_ids, bounds=(scaled_output_bounds[0], scaled_output_bounds[1])
+        )
+    else:
+        block.intermediate_output = pe.Var(
+            tree_ids, bounds=(unscaled_output_bounds[0], unscaled_output_bounds[1])
+        )
 
     # Create a disjunct for each leaf containing the bound constraints
     # and the linear model expression.
@@ -302,7 +311,8 @@ def _add_hybrid_formulation_to_block(block, model_definition, input_vars, output
         output_vars -- output variable of the linear tree model
     """
     leaves = model_definition.leaves
-    input_bounds = model_definition.scaled_input_bounds
+    scaled_input_bounds = model_definition.scaled_input_bounds
+    unscaled_input_bounds = model_definition.unscaled_input_bounds
     n_inputs = model_definition.n_inputs
 
     # The set of trees
@@ -318,13 +328,16 @@ def _add_hybrid_formulation_to_block(block, model_definition, input_vars, output
 
     # Use the input_bounds and the linear models in the leaves to calculate
     # the lower and upper bounds on the output variable. Required for Pyomo.GDP
-    output_bounds = _build_output_bounds(model_definition, input_bounds)
+    scaled_output_bounds = _build_output_bounds(model_definition, scaled_input_bounds)
+    unscaled_output_bounds = _build_output_bounds(
+        model_definition, unscaled_input_bounds
+    )
 
     # Ouptuts are automatically scaled based on whether inputs are scaled
-    block.outputs.setub(output_bounds[1])
-    block.outputs.setlb(output_bounds[0])
-    block.scaled_outputs.setub(output_bounds[1])
-    block.scaled_outputs.setlb(output_bounds[0])
+    block.outputs.setub(scaled_output_bounds[1])
+    block.outputs.setlb(scaled_output_bounds[0])
+    block.scaled_outputs.setub(unscaled_output_bounds[1])
+    block.scaled_outputs.setlb(unscaled_output_bounds[0])
 
     # Create the intermeditate variables. z is binary that indicates which leaf
     # in tree t is returned. intermediate_output is the output of tree t and