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Most ML algorithms make predictions concerning future data which cannot be deduced from already observed data. The inductive bias of an algorithm is what choses between different possible future predictions. A strong form of inductive bias is the learner’s choice of hypothesis/model space which is sometimes called declarative bias. In the case of Bayesian analysis, the inductive bias is encapsulated in the prior distribution.
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(2017). Inductive Bias. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_390
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_390
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