diff --git a/trainer.py b/trainer.py
index db53208..c2989f3 100644
--- a/trainer.py
+++ b/trainer.py
@@ -645,19 +645,19 @@ class BinaryTuner:
 #            expected_value = expected_value[1]
 #        shap_values = explainer.shap_values(X_test)[1]
         self.logger.info("Columns: {}".format(Xbase.columns))
-#        eng_columns = ['sex', 'family hist', 'age diag', 'BMI', 'base glu', 'glu 120','HbA1c']
-        esp_columns = ['sexo', 'hist fam', 'edad diag', 'IMC', 'glu ayu', 'glu 120','A1c']
+#        label_columns = ['sex', 'family hist', 'age diag', 'BMI', 'base glu', 'glu 120','HbA1c']
+        label_columns = ['sexo', 'hist fam', 'edad diag', 'IMC', 'glu ayu', 'glu 120','A1c']
 
         explainer = shap.Explainer(model.predict, X_train, seed=seed)
         shap_values = explainer(X_model)
 
         exp = shap.Explanation(shap_values,
                           data=X_model,
-                          feature_names=esp_columns)
+                          feature_names=label_columns)
 
         #
 #        shap.plots.initjs()
-        shap.plots.decision(exp.base_values[0], exp.values, features=eng_columns, show=False)
+        shap.plots.decision(exp.base_values[0], exp.values, features=label_columns, show=False)
 #        shap.plots.force(exp.base_values, exp.values, feature_names=Xbase.columns, show=False)
 #        shap.plots.force(exp.base_values[0], exp.values[0, :], feature_names=Xbase.columns, matplotlib=True, show=False)
 #        shap.plots.force(expected_values[0], shap_values.values, Xbase.columns , show=False)
@@ -671,7 +671,7 @@ class BinaryTuner:
 
         exp = shap.Explanation(shap_values,
                           data=X_explain,
-                          feature_names=eng_columns)
+                          feature_names=label_columns)
 
         for i in range(5):
             shap.plots.waterfall(exp[i], show=False)