Although it is usually possible to compute the correlation coefficient between two vectors of equal length, it is not easy to interpret a given value of this coefficient. This article empirically demonstrates that under certain circumstances, an inverse linear relationship exists between the correlation coefficient and classification error. This relationship makes it possible to interpret every possible correlation value in terms of a corresponding rate of classification error.

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