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Class imbalance is not a disease of your data. It is a fact about the world: the outcome you care about is rare. “Correcting” it by throwing away controls or synthesizing cases does not make your model better. It makes your probabilities wrong, and then you have to do more work to make them wrong in the opposite direction.
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This is part of a series on the BBTools suite. The demo data come from the BBDuk post, and the BBMap post removed the phiX spike-in.
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If your accuracy score can be improved by a model that is calibrated worse, the score is broken. Proportion classified correctly is exactly such a score. It is not a minor imperfection to note in the discussion section. It is a metric that will, given the chance, hand you the wrong model and a straight face.
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This is part of a series on the BBTools suite. The overview introduces the tools and the BBDuk post builds the demo data used here.
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A classifier forces a choice. A probability model quantifies a tendency and hands the choice back to whoever is actually equipped to make it. In most of the problems I work on, the second thing is what I want, and the first thing is what I built by accident.
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This is part of a series on the BBTools suite. If you haven’t read the overview post, that’s a good place to start.
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