Training a machine learning model can be computationally quite expensive, as we have seen in Chapter 8, Applying Machine Learning to Sentiment Analysis. Surely, we don't want to train our model every time we close our Python interpreter and want to make a new prediction or reload our web application? One option for model persistence is Python's in-built pickle module (https://docs.python.org/3.4/library/pickle.html), which allows us to serialize and de-serialize Python object structures to compact byte code, so that we can save our classifier in its current state and reload it if we want to classify new samples without needing to learn the model from the training data all over again. Before you execute the following code, please make sure that you have trained the out-of-core logistic regression model from the last section of Chapter 8, Applying Machine Learning to Sentiment Analysis, and have it ready in your current Python session:
>>>...
