> a good scientist doesn't through away a model with predicting power, regardless of how it's derived. a good scientist has to verify that it is actually > predicting (not post-hoc rationalizing/adjustment) > falsifiable (the model shouldn't be interpretive to be correct in all outcomes) > not actually just getting lucky (a bad model can be correct a few times, coin tossing for discrete number of events has a chance of 100% accuracy) because trusting in models as functioning black boxes can lead you to some really insane places (t. data engineer)
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