Achieving model persistence
When we train a model, it would be nice if we could save it as a file so that it can be used later by simply loading it again.
How to do it…
Let's see how to achieve model persistence programmatically:
- Add the following lines to
regressor.py
:import cPickle as pickle output_model_file = 'saved_model.pkl' with open(output_model_file, 'w') as f: pickle.dump(linear_regressor, f)
- The regressor object will be saved in the
saved_model.pkl
file. Let's look at how to load it and use it, as follows:with open(output_model_file, 'r') as f: model_linregr = pickle.load(f) y_test_pred_new = model_linregr.predict(X_test) print "\nNew mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred_new), 2)
- Here, we just loaded the ...
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