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It is usually the statistician using the classifier
that is the problem (for picking the wrong tool for the particular problem at
hand, or for using it incorrectly). The "no free lunch" theorems show
there is no a-priori distinction between classifiers (which works best/worst
depends on the data), so I'd say there isn't a worst classifier in a general
sense. However, some classifiers are easier to shoot yourself in the foot with
than others, in which case, I would nominate artificial neural networks (the
over-fit very easily and need proper architecture selection and none of the
solutions to such problems are reliable)..
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