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These differences are interpreted as a kind of bias. Mathematically, the spectrum bias is a sampling bias and not a traditional statistical bias; this has led some authors to refer to the phenomenon as spectrum effects, [3] whilst others maintain it is a bias if the true performance of the test differs from that which is 'expected'. [2]
Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test. For example, a high prevalence of disease in a study population increases positive predictive values, which will cause a bias between the prediction values and the real ones. [4]
In statistics, the bias of an estimator (or bias function) is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. In statistics, "bias" is an objective property of an estimator.
Lead time bias happens when survival time appears longer because diagnosis was done earlier (for instance, by screening), irrespective of whether the patient lived longer. Lead time is the duration of time between the detection of a disease (by screening or based on new experimental criteria) and its usual clinical presentation and diagnosis ...
The frequency principle/spectral bias is a phenomenon observed in the study of artificial neural networks (ANNs), specifically deep neural networks (DNNs).It describes the tendency of deep neural networks to fit target functions from low to high frequencies during the training process.
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. [1]
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Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, ... [123] fall within a large spectrum, and can be ...