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Data Science (DS) algorithms interpret outcomes of empirical experiments with random influences. Often, such algorithms are cascaded to long processing pipelines especially in biomedical applications. The validation of such pipelines poses an open question since data compression of the input should preserve as much information as possible to distinguish between possible outputs. Starting with a minimum description length argument for model selection we motivate a localization criterion as a lower bound that achieves information theoretical optimality. Uncertainty in the input causes a rate distortion tradeoff in the output when the DS algorithm is adapted by learning. We present design choices for algorithm selection and sketch a theory of validation. The concept is demonstrated in neuroscience applications of diffusion tensor imaging for tractography and brain parcellation.
