diff --git a/bayesclass/clfs.py b/bayesclass/clfs.py
index c28066d..6dc7a32 100644
--- a/bayesclass/clfs.py
+++ b/bayesclass/clfs.py
@@ -39,6 +39,14 @@ class BayesBase(BaseEstimator, ClassifierMixin):
             return len(self.dag_), len(self.dag_.edges())
         return 0, 0
 
+    @staticmethod
+    def default_feature_names(num_features):
+        return [f"feature_{i}" for i in range(num_features)]
+
+    @staticmethod
+    def default_class_name():
+        return "class"
+
     def _check_params_fit(self, X, y, expected_args, kwargs):
         """Check the common parameters passed to fit"""
         # Check that X and y have correct shape
@@ -48,8 +56,8 @@ class BayesBase(BaseEstimator, ClassifierMixin):
         self.classes_ = unique_labels(y)
         self.n_classes_ = self.classes_.shape[0]
         # Default values
-        self.class_name_ = "class"
-        self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
+        self.class_name_ = self.default_class_name()
+        self.features_ = self.default_feature_names(X.shape[1])
         for key, value in kwargs.items():
             if key in expected_args:
                 setattr(self, f"{key}_", value)
@@ -458,7 +466,11 @@ class KDBNew(KDB):
     def fit(self, X, y, **kwargs):
         self.discretizer_ = FImdlp(n_jobs=1)
         Xd = self.discretizer_.fit_transform(X, y)
-        features = kwargs["features"]
+        features = (
+            kwargs["features"]
+            if "features" in kwargs
+            else self.default_feature_names(Xd.shape[1])
+        )
         self.compute_kwargs(Xd, y, kwargs)
         # Build the model
         super().fit(Xd, y, **kwargs)
@@ -475,7 +487,12 @@ class KDBNew(KDB):
             features[i]: np.unique(Xd[:, i]).tolist()
             for i in range(Xd.shape[1])
         }
-        states[kwargs["class_name"]] = np.unique(y).tolist()
+        class_name = (
+            kwargs["class_name"]
+            if "class_name" in kwargs
+            else self.default_class_name()
+        )
+        states[class_name] = np.unique(y).tolist()
         kwargs["state_names"] = states
         self.kwargs_ = kwargs