diff --git a/bayesclass/clfs.py b/bayesclass/clfs.py
index b9cf8de..0126c36 100644
--- a/bayesclass/clfs.py
+++ b/bayesclass/clfs.py
@@ -475,7 +475,6 @@ class KDBNew(KDB):
         return self.estimator.fit(X, y, **kwargs)
 
     def predict(self, X):
-        self.plot()
         return self.estimator.predict(X)
 
 
@@ -492,14 +491,14 @@ class Proposal:
         # Build the model
         super(self.class_type, self.estimator).fit(self.Xd, y, **kwargs)
         self.check_integrity("f", self.Xd)
-        # # Local discretization based on the model
-        # features = kwargs["features"]
-        # # assign indices to feature names
-        # self.idx_features_ = dict(list(zip(features, range(len(features)))))
-        # upgraded, self.Xd = self._local_discretization()
-        # if upgraded:
-        #     kwargs = self.update_kwargs(y, kwargs)
-        #     super(self.class_type, self.estimator).fit(self.Xd, y, **kwargs)
+        # Local discretization based on the model
+        features = kwargs["features"]
+        # assign indices to feature names
+        self.idx_features_ = dict(list(zip(features, range(len(features)))))
+        upgraded, self.Xd = self._local_discretization()
+        if upgraded:
+            kwargs = self.update_kwargs(y, kwargs)
+            super(self.class_type, self.estimator).fit(self.Xd, y, **kwargs)
 
     def predict(self, X):
         self.check_integrity("p", self.discretizer.transform(X))
@@ -534,14 +533,14 @@ class Proposal:
         """Discretize each feature with its fathers and the class"""
         res = self.Xd.copy()
         upgraded = False
-        print("-" * 80)
+        # print("-" * 80)
         for idx, feature in enumerate(self.estimator.feature_names_in_):
             fathers = self.estimator.dag_.get_parents(feature)
             if len(fathers) > 1:
-                print(
-                    "Discretizing " + feature + " with " + str(fathers),
-                    end=" ",
-                )
+                # print(
+                #     "Discretizing " + feature + " with " + str(fathers),
+                #     end=" ",
+                # )
                 # First remove the class name as it will be added later
                 fathers.remove(self.estimator.class_name_)
                 # Get the fathers indices
@@ -550,12 +549,12 @@ class Proposal:
                 res[:, idx] = self.discretizer.join_fit(
                     target=idx, features=features, data=self.Xd
                 )
-                print(self.discretizer.y_join[:5])
+                # print(self.discretizer.y_join[:5])
                 upgraded = True
         return upgraded, res
 
     def check_integrity(self, source, X):
-        print(f"Checking integrity of {source} data")
+        # print(f"Checking integrity of {source} data")
         for i in range(X.shape[1]):
             if not set(np.unique(X[:, i]).tolist()).issubset(
                 set(self.state_names_[self.features_[i]])