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Assessment of Maching Learning Classifiers

I heard another interesting episode of the Data Skeptic podcast. They were discussing how a classifier could be assessed (episode 121). Many machine learning models are so complex that a human being can't really interpret the meaning of the model. This can lead to problems.

They gave an example of a problem where they had a bunch of posts from two discussion boards. One was atheist and the other board was composed of Christians. They tried to classify each post as being from one or the other board. There was one poster who posted heavily on the Christian board. His name was Keith. Sadly, the model learned that if the person who was posting was named Keith, then they were Christian. The problem is that this isn't very useful for prediction. It's an artifact of the input data. Even cross-validation would eliminate this problem. A human being can see the issue, but a model can't.

In any event, the proposed solution was to build interpretable models in local areas of the predictions. If a series of these local models make sense to a human being, then it is likely that the overall model is a good classifier for prediction problems.

I thought this was interesting as it really shows the need for theory in all kinds of modeling problems. There ought to be some substantive theory in which at least elements of the model are grounded.

I also had the opportunity to re-read Groves, Cialdini, and Couper and the decision to participate in a survey. There is some theory development on the survey response process, but there is also room to grow in this area. I wouldn't want an emphasis on using machine learning techniques for weight development or other purposes to distract us from the need to keep working on the theory development problem as well.


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