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Calibration by group, error rate parity, sufficiency, and separation

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In the field of algorithmic fairness, it is well known that there are several definitions of fairness that are impossible to reconcile except in (practically irrelevant) corner cases. In this context, I have recently tried to wrap my head around why – intuitively – it is impossible for any classifier to achieve separation and sufficiency at the same time (unless either the classifier is a perfect classifier or there are no base rate differences between groups – we will get to these details in a minute). Since part of my troubles arose from a misunderstanding of what separation and sufficiency actually mean, let us start by revisiting their definitions.

Maximum likelihood, cross-entropy, risk minimization

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really, yet another post about about maximum likelihood (ML) estimation? Well – yes; I could not find a source that summarized exactly the things I needed to know, so here it is. What will you find?

Beautiful boxplots in pgfplots

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Recently, I wanted to create boxplots from a data file for a paper I was writing using pgfplots. Turns out that’s more difficult than expected, especially since the (otherwise very useful) documentation is a bit meager in this point.