Putting it all together

We can use the existing parameter space and the existing classifier from our previous experiments—all we need to do is refit it on our new data. By default, training in scikit-learn is done from scratch—subsequent calls to fit() will discard any previous information.

Note

There is a class of algorithms called online learning that update the training with new samples and don't restart their training each time. 

As before, we can compute our scores by using cross_val_score and print the results. The code is as follows:

scores = cross_val_score(pipeline, documents, classes, scoring='f1') 

print("Score: {:.3f}".format(np.mean(scores)))

The result is 0.683, which is a reasonable result for such a messy dataset. Adding more data (such as increasing max_docs_author in the dataset loading) can improve these results, as will improving the quality of the data with extra cleaning.