[DOC] Update guide.rst

Author: xuyxu

docs/guide.rst

@@ -1,14 +1,43 @@
 Guidance
 ========
 
-This page provides useful instructions on choosing the appropriate ensemble method.
+This page provides useful instructions on how to choose the appropriate ensemble method for your deep learning model.
 
 Check Your Model
 ----------------
 
+A good rule-of-thumb is to check the performance of your deep learning model first. Below are two important aspects that you should pay attention to:
+
+* What is the final performance of your model ?
+* Does your model suffer from the over-fitting problem ?
+
+To check these two aspects, it is recommended to evaluate the performance of your model on the :obj:`test_loader` after each training epoch.
+
+.. tip::
+    Using Ensemble-PyTorch, you can pass your model to the :class:`Fusion` or :class:`Voting` with the argument ``n_estimators`` set to ``1``. The behavior of the ensemble should be the same as a single model.
+
+If the performance of your model is relatively good, for example, the testing accuracy of your LeNet-5 CNN model on MNIST is over 99%, the conclusion on the first point is that it is not likely that your model suffers from the under-fitting problem. You could skip the section :ref:`under_fit`.
+
+.. _under_fit:
+
 Under-fit
 ---------
 
+If the performance of your model is unsatisfactory, you can try out the :class:`Gradient Boosting` related ensemble methods. :class:`Gradient Boosting` focuses on reducing the bias term from the perspective of `Bias-Variance Decomposition <https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff>`__, it usually works well when your deep learning model is a weak learner on the dataset.
+
+Below is the pros and cons on using :class:`Gradient Boosting`:
+
+* Pros:
+    - You can have a much higher improvements than using other ensemble methods
+* Cons:
+    - Relatively longer training time
+    - Suffer from the over-fitting problem if the value of ``n_estimators`` is large
+
+.. tip::
+    :class:`Gradient Boosting` in Ensemble-PyTorch supports the early stopping to alleviate the over-fitting. To use early stopping, you need to set the input argument ``test_loader`` and ``early_stopping_rounds`` when calling the :meth:`fit` function of :class:`Gradient Boosting`. In additional, using a small ``shrinkage_rate`` when declaring the model also helps to alleviate the over-fitting problem.
+
+.. _over_fit:
+
 Over-fit
 --------