Bias of Supervised Models in Statistical Learning
Bias is an error that occurs when a model is unable to accurately represent the true 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 . Its flexibility is limited and often underfits the data. More flexible methods result in less bias since they are able to more accurately represent the true .
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