diff --git a/minigrida/descriptors/pixel.py b/minigrida/descriptors/pixel.py
index ff37bef..ba96ac7 100644
--- a/minigrida/descriptors/pixel.py
+++ b/minigrida/descriptors/pixel.py
@@ -33,5 +33,6 @@ def run(gt, rasters, coords, remove=None):
     X = np.concatenate(X)
     y = np.concatenate(y)
     groups = np.concatenate(groups)
+    Xn = rasters[0].keys()
 
-    return X, y, groups
+    return X, y, groups, Xn
diff --git a/minigrida/protocols/jurse2.py b/minigrida/protocols/jurse2.py
index 8238b16..8fa908a 100644
--- a/minigrida/protocols/jurse2.py
+++ b/minigrida/protocols/jurse2.py
@@ -21,6 +21,7 @@ class Jurse2(Protocol):
 
     def __init__(self, expe):
         super().__init__(expe, self.__class__.__name__)
+        self._results = {}
 
     def _run(self):
         self._log.info('Load data')
@@ -44,9 +45,7 @@ class Jurse2(Protocol):
         self._log.info('Run metrics')
         metrics = self._run_metrics(classification, descriptors)
 
-        results = {}
-        results['metrics'] = metrics
-        self._results = results
+        self._results['metrics'] = metrics
 
     def _load_data(self):
         data_loader = self._expe['data_loader']
@@ -65,7 +64,7 @@ class Jurse2(Protocol):
         return att
 
     def _compute_classification(self, descriptors):
-        X, y, groups = descriptors
+        X, y, groups, Xn = descriptors
 
         # CrossVal and ML
         cv = self._expe['cross_validation']
@@ -76,6 +75,7 @@ class Jurse2(Protocol):
 
         y_pred = np.zeros_like(y)
 
+        cl_feature_importances = []
         cvi = cross_val(**cv['parameters'])
         for train_index, test_index in cvi.split(X, y, groups):
             cli = classifier(**cl['parameters'])
@@ -86,6 +86,14 @@ class Jurse2(Protocol):
             self._log.info('  - predict')
             y_pred[test_index] = cli.predict(X[test_index])
 
+            cl_feature_importances += [classifier.feature_importances_.copy()]
+
+        cl_feature_importances = np.array(cl_feature_importances)
+        self._results['features'] = {
+                'name': Xn,
+                'importance': cl_feature_importances.tolist()
+                }
+
         return y_pred
 
     def _get_results(self):