Serializing fitted scikit-learn estimators
Training a machine learning model can be computationally expensive, as you saw in Chapter 8, Applying Machine Learning to Sentiment Analysis. Surely, we don't want to retrain 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/3.7/library/pickle.html), which allows us to serialize and deserialize Python object structures to compact bytecode so that we can save our classifier in its current state and reload it if we want to classify new, unlabeled examples, without needing the model to learn 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 and have it ready in your current Python session:
>>> import pickle
>>>...
