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You may be correct technically. I'm not familiar with the formal definition of "confidence intervals" in statistics. Informally we often speak of confidence a
by tgflynn 10y ago
You may be correct technically. I'm not familiar with the formal definition of "confidence intervals" in statistics. Informally we often speak of confidence as an estimate of the conditional probability P( C | x ) where C is the class returned by a classifier and x is the input. That's what I thought the parent comment was referring to but I may have been mistaken.
EDIT: For my own information what would knowing the confidence intervals tell you that P( C | x ) wouldn't ?
- versteegen 10y agoIf C is binary or categorical, then confidence intervals don't make sense to talk about (although you could still talk about them if C is integer valued), and the equivalent thing is P(C|x). Unless I guess if you have a posterior distribution over distribution... The definition of a Q (e.g Q = 0.95) confidence interval a < X < b is just that P(a < X < b) = Q. It's not even necessary that the interval contain the most probable value of X, but when you actually calculate them to want to tack on extra constraints to prevent stuff like that, like minimising the length of the interval.