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Mainly because schooling sets are finite and the future is uncertain, learning theory commonly would not yield guarantees from the overall performance of algorithms. As a substitute, probabilistic bounds on the overall performance are pretty common. The bias–variance decomposition is one method to quantify generalization error.¹Each College esta

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