Understanding the Bias-Variance Trade-off

George Box once said, “All models are wrong, but some are useful.” From a supervised machine learning perspective, all models have errors, and to make our models useful, we have to minimize such errors. More specifically, we have to minimize two major sources of error: bias and variance. Prior to applying a machine learning algorithm,…
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February 14, 2019 0