how to call feature importances and contributions

docs/source/tutorial.md

@@ -128,4 +128,18 @@ CB_est = Pipeline([("binning", binner), ("CB", est)])
 CB_est.fit(X_train, y)
 
 yhat = CB_est.predict(X_test)
-```
+```
+
+## Feature Importances
+To get a dictionary with the relative importances of the different model
+features in the training:
+```python
+CB_est.get_feature_importances()
+```
+
+## Individual Explainability
+To get a dictionary with the contributions of the different model features to
+the individual predictions of a given data set:
+```python
+CB_est.get_feature_contributions(X_test)
+```