diff --git a/src/omlt/linear_tree/lt_definition.py b/src/omlt/linear_tree/lt_definition.py
index e45274fd..74882666 100644
--- a/src/omlt/linear_tree/lt_definition.py
+++ b/src/omlt/linear_tree/lt_definition.py
@@ -1,5 +1,6 @@
 import numpy as np
 import lineartree
+import sklearn
 
 
 class LinearTreeDefinition:
@@ -266,7 +267,10 @@ def _parse_tree_data(model, input_bounds):
     # Include checks to ensure that the input dict is the model summary which
     # is obtained by calling the summary() method contained within the
     # linear-tree package (e.g. dict = model.summary())
-    if isinstance(model, lineartree.lineartree.LinearTreeRegressor) is True:
+    if (
+        isinstance(model, lineartree.lineartree.LinearTreeRegressor) is True
+        or isinstance(model, lineartree.lineartree.LinearTreeClassifier) is True
+    ):
         leaves = model.summary(only_leaves=True)
         splits = model.summary()
     elif isinstance(model, dict) is True:
@@ -294,11 +298,43 @@ def _parse_tree_data(model, input_bounds):
         raise TypeError("Model entry must be dict or linear-tree instance")
 
     # This loop adds keys for the slopes and intercept and removes the leaf
-    # keys in the splits dictionary
+    # keys in the splits dictionary. For LinearTreeClassifier, check if
+    # the model in the leaf is a DummyClassifier. If so, use the information
+    # in the prior to determine whether the intercept is 1, or -1. Otherwise
+    # use the slope/intercept information in the RidgeClassifier or
+    # LinearTreeRegressor classes
     for leaf in leaves:
         del splits[leaf]
-        leaves[leaf]["slope"] = list(leaves[leaf]["models"].coef_)
-        leaves[leaf]["intercept"] = leaves[leaf]["models"].intercept_
+        if isinstance(model, lineartree.lineartree.LinearTreeClassifier) is True:
+            num_classes = len(leaves[leaf]["classes"])
+        else:
+            num_classes = 999
+
+        if num_classes < 2:
+            class_val = int(leaves[leaf]["classes"][0])
+            leaves[leaf]["slope"] = list(np.zeros(len(input_bounds.keys())))
+            if class_val == 0:
+                leaves[leaf]["intercept"] = -1
+            else:
+                leaves[leaf]["intercept"] = 1
+        else:
+            model_in_leaf = leaves[leaf]["models"]
+            if isinstance(model_in_leaf, sklearn.dummy.DummyClassifier):
+                prior = model_in_leaf.class_prior_
+                leaves[leaf]["slope"] = list(np.zeros(len(input_bounds.keys())))
+                if len(prior) < 2:
+                    pred_val = int(model_in_leaf.predict([0])[0])
+                    if pred_val == 0:
+                        leaves[leaf]["intercept"] = -1
+                    else:
+                        leaves[leaf]["intercept"] = 1
+                elif prior[0] <= prior[1]:
+                    leaves[leaf]["intercept"] = 1
+                else:
+                    leaves[leaf]["intercept"] = -1
+            else:
+                leaves[leaf]["slope"] = list(model_in_leaf.coef_.reshape((-1,)))
+                leaves[leaf]["intercept"] = model_in_leaf.intercept_.reshape((-1,))[0]
 
     # This loop creates an parent node id entry for each node in the tree
     for split in splits: