Summary
We made it! For a very noisy dataset, we built a classifier that suits part of our goal. Of course, we had to be pragmatic and adapt our initial goal to what was achievable. But on the way, we learned about the strengths and weaknesses of the nearest neighbor and logistic regression algorithms. We learned how to extract features, such as LinkCount, NumTextTokens, NumCodeLines, AvgSentLen, AvgWordLen, NumAllCaps, NumExclams, and NumImages, and how to analyze their impact on the classifier's 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 algorithms, in the next chapter we will get familiar with yet another simple yet powerful classification algorithm: Naive Bayes. Along the way, we will also learn how to use some more convenient tools from Scikit-learn.
