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For those missing the context, this model was a reductio ad absurdum response to https://twitter.com/MartinDaubney/status/1314590150245183488

You can overfit any small dataset using a relatively simple error reduction method.

My model is a hashmap that contains my training data. Missing keys are forecast uniformly randomly. It perfectly matches 100% of my training data.

Exactly. The troubling part is that even with overfitting his model still missed two elections.

That’s probably intentional, no one gets 100% on the exam.

So... the bug is a feature?