Why is there a trade-off between bias and variance in supervised learning? Why can't we have best of both worlds?

datascience.stackexchange https://datascience.stackexchange.com/questions/58270

문제

The bias-variance trade-off is like a law in machine learning. You cannot have the best of both worlds. What is it about supervised learning in machine learning that makes it impossible to satisfy the two at the same time?

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