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摘要: The bias-variance tradeoff, part 2 of 3

In Part 1, we covered much of the basic terminology as well as a few key insights about the bias-variance formula (MSE = Bias² + Variance), including this paraphrase from Anna Karenina:

All perfect models are alike, but each unhappy model can be unhappy in its own way.

To make the most of this article, I suggest taking a look at Part 1 to make sure you’re well-situated to absorb this one.

摘要: The AI bias trouble starts — but doesn’t end — with definition. “Bias” is an overloaded term which means remarkably different things in different contexts.