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Weird Kaggle and the Superiority of Books

I recently entered a Kaggle competition to brush up on some modeling skills.1 The analysis problem is a pretty typical clinical prediction question. My final product is, well, "final product" is an extremely charitable description of it.2 But despite this it was a fascinating and worthwhile experience, full of interesting questions to ponder, such as "what is the fundamental difference between statistics and machine learning?" and "do they realize their evaluation metric is pretty silly?" The following are some notes from my project log.3 Wait, you want me to maximize what? As I read the rules, they started off normally.

It evaluates submissions based on concordance, which is just a scoring method that rewards the model for correctly ranking who will survive the longest (it doesn't matter if you are wrong about how much longer). So to sum up, it seems like the statistics approach is more about carefully crafted models that you try to understand more deeply: you can apply background knowledge (and priors), and you can mathematically express theories and test them out. The problem is that the loss function is no longer an independent calculation for each data point (it becomes non-decomposable), which is not supported by the library and likely comes with a big cost in terms of optimizing the computation.

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