https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229

Written by: Seema Singh
Publised on: May 21, 2018

University of Michigan - Ann Arbor

Bias is an error that occurs when a model is unable to accurately represent the true $f$ as a result of underlying assumptions made to simplify the model. For example, using linear regression for a relationship that is not linear in nature has a large amount of bias since no linear estimate will result in a model that accurately captures the non-linear nature of $f$. Its flexibility is limited and often underfits the data. More flexible methods result in less bias since it is able to more accurately represent the true $f$.

Bias of Supervised Models in Statistical Learning

towardsdatascience.com: Understanding the Bias-Variance Tradeoff

The left plot shows fitting a straight line to the data. This is not a very good fit. So, it is underfitting the data and we call it high bias. On the opposite end, if you fit an incredibly complex classifier, maybe you can fit the data perfectly, but that doesn't look like a great fit either. So there's a classifier of high variance and this is overfitting the data. And there might be some classifier in between, with a medium level of complexity, that maybe fits it correctly.

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