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Used (1) for discrete classification (if an instance's predicted probability exceeds a threshold, classify as TRUE, otherwise FALSE), or (2) for discretizing/binning continuous data. *If you are tempted to use this tag, PLEASE read the tag wiki!*
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How to calculate probability percentage for logistic regression with threshold
However, how do we interpret a probability in light of a threshold? … For example, if a threshold is 0.195, and a user has a probability of 0.0975 are they:
50% likely to respond (1) since they are 50% towards the threshold? …