diff --git a/BlackBoxAuditing/model_factories/DecisionTree.py b/BlackBoxAuditing/model_factories/DecisionTree.py
index ada5e22a..10c7b23c 100644
--- a/BlackBoxAuditing/model_factories/DecisionTree.py
+++ b/BlackBoxAuditing/model_factories/DecisionTree.py
@@ -133,7 +133,7 @@ def list_to_tf_input(data, response_index, num_outcomes):
   """
   Separates the outcome feature from the data and creates the onehot vector for each row.
   """
-  matrix = np.matrix([row[:response_index] + row[response_index+1:] for row in data])
+  matrix = np.array([row[:response_index] + row[response_index+1:] for row in data])
   outcomes = np.asarray([row[response_index] for row in data], dtype=np.uint8)
   outcomes_onehot = (np.arange(num_outcomes) == outcomes[:, None]).astype(np.float32)
 
diff --git a/BlackBoxAuditing/model_factories/NeuralNetwork.py b/BlackBoxAuditing/model_factories/NeuralNetwork.py
index 591f6e18..bc943fd9 100644
--- a/BlackBoxAuditing/model_factories/NeuralNetwork.py
+++ b/BlackBoxAuditing/model_factories/NeuralNetwork.py
@@ -155,7 +155,7 @@ def list_to_tf_input(data, response_index, num_outcomes):
   """
   Separates the outcome feature from the data and creates the onehot vector for each row.
   """
-  matrix = np.matrix([row[:response_index] + row[response_index+1:] for row in data])
+  matrix = np.array([row[:response_index] + row[response_index+1:] for row in data])
   outcomes = np.asarray([row[response_index] for row in data], dtype=np.uint8)
   outcomes_onehot = (np.arange(num_outcomes) == outcomes[:, None]).astype(np.float32)
 
diff --git a/BlackBoxAuditing/model_factories/SKLearnModelVisitor.py b/BlackBoxAuditing/model_factories/SKLearnModelVisitor.py
index 8084bcdf..bc91f278 100644
--- a/BlackBoxAuditing/model_factories/SKLearnModelVisitor.py
+++ b/BlackBoxAuditing/model_factories/SKLearnModelVisitor.py
@@ -8,7 +8,7 @@ def __init__(self, trained_classifier, label_index):
         self.label_index = label_index
 
     def get_Xy(self, test_set):
-        X = np.matrix([row[:self.label_index] + row[self.label_index+1:] for row in test_set])
+        X = np.array([row[:self.label_index] + row[self.label_index+1:] for row in test_set])
         y = np.asarray([row[self.label_index] for row in test_set])
         return X, y
 
diff --git a/BlackBoxAuditing/model_factories/SVM.py b/BlackBoxAuditing/model_factories/SVM.py
index 919cac39..c9c1e545 100644
--- a/BlackBoxAuditing/model_factories/SVM.py
+++ b/BlackBoxAuditing/model_factories/SVM.py
@@ -139,7 +139,7 @@ def list_to_tf_input(data, response_index, num_outcomes):
   """
   Separates the outcome feature from the data.
   """
-  matrix = np.matrix([row[:response_index] + row[response_index+1:] for row in data])
+  matrix = np.array([row[:response_index] + row[response_index+1:] for row in data])
   outcomes = np.asarray([row[response_index] for row in data], dtype=np.uint8)
 
   return matrix, outcomes