ship itlet' assume we want to integrate this classifier into our site what we definitely do not want is to train the classifier each time we start the classification service insteadwe can simply serialize the classifier after training and then deserialize it on that siteimport pickle pickle dump(clfopen("logreg dat"" ")clf pickle load(open("logreg dat"" ")congratulationsthe classifier is now ready to be used as if it had just been trained summary we made itfor very noisy datasetwe built classifier that suits part of our goal of coursewe had to be pragmatic and adapt our initial goal to what was achievable but on the waywe learned about the strengths and weaknesses of the nearest neighbor and logistic regression algorithms we learned how to extract featuressuch as linkcountnumtexttokensnumcodelinesavgsentlenavgwordlennumallcapsnumexclamsand numimagesand how to analyze their impact on the classifier' performance but what is even more valuable is that we learned an informed way of how to debug badly performing classifiers this will help us in the future to come up with usable systems much faster after having looked into the nearest neighbor and logistic regression algorithmsin the next we will get familiar with yet another simple yet powerful classification algorithmnaive bayes along the waywe will also learn how to use some more convenient tools from scikit-learn