Error Distributions

From The Foundation for Best Practices in Machine Learning

Error Distributions


Document and assess error and/or residual distributions along as many dimensions and/or subsets as is practically feasible. If distributions are too broad and/or too unequal between subsets, improve Model(s).


To (a) assess and control for performance influence of data points and/or groups; (b) assess and control for the distribution of errors to influence - (i) performance robustness as a function of data drift, (ii) the systematic performance of minority data-subsets, and (iii) the risks of unacceptable errors and/or catastrophic failure; and (c) highlight associated risks that might occur in the Product Lifecycle.

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