diff --git a/trainer.py b/trainer.py
index 92d4ed9..db53208 100644
--- a/trainer.py
+++ b/trainer.py
@@ -645,14 +645,15 @@ 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']
+#        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']
 
         explainer = shap.Explainer(model.predict, X_train, seed=seed)
         shap_values = explainer(X_model)
 
         exp = shap.Explanation(shap_values,
                           data=X_model,
-                          feature_names=eng_columns)
+                          feature_names=esp_columns)
 
         #
 #        shap.plots.initjs()
@@ -661,6 +662,7 @@ class BinaryTuner:
 #        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)
         plt.title(r"{0}".format(modelname))
+        plt.ylabel("Respuesta del Modelo: 0 Negativo, 1 Positivo")
         plt.savefig("{}/shap_{}_{}_{}.png".format(self.name, modelname, dataset, seed),dpi=150, bbox_inches='tight')
         plt.close()