When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to “bias” and error due to “variance”. There is a tradeoff between a model’s ability to minimize bias and variance. Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting.
http://scott.fortmann-roe.com/docs/BiasVariance.html
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