Sketching our roadmap

We will build a system using real data that is very noisy. This chapter is not for the fainthearted, as we will not arrive at the golden solution for a classifier that achieves 100 percent accuracy. This is because even humans often disagree whether an answer was good or not (just look at some of the comments on the stackoverflow.com website). Quite the contrary, we will find out that some problems like this one are so hard that we have to adjust our initial goals on the way. But on that way, we will start with the nearest neighbor approach, find out why it is not very good for the task, switch over to logistic regression, and arrive at a solution that will achieve a good prediction quality but on a smaller part of the answers. Finally, we will spend some time on how to extract the winner to deploy it on the target system.