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Figure 1 | BioMedical Engineering OnLine

Figure 1

From: Machine learning, medical diagnosis, and biomedical engineering research - commentary

Figure 1

Apparent accuracy of classifiers ( ACC ) applied to synthetic training sets of equal numbers of “healthy” and “ill” subjects, with 10 attributes for each subject created using a random number generator. The horizontal axis is the ratio of the number of “ill” subjects to number of attributes (10 in each case). The increase in accuracy (ACC in the vertical axis) for smaller training sets is a result of use of a too-small training set, coupled with post-hoc theorizing. Since the set had an equal number of “patients” and “healthy” individuals, the accuracy of the classifier should be 50% as expected by chance.

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