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Recently there has been discussion about the distinction between classification and prediction. In particular it concerns the case where outcome $Y\sim Bern(p)$. Usually $p$ depends on covariates but we can ignore that here. The claim is that a prediction must be an estimate $\hat{p}$ of the probability $P(Y=1)$, and a classification, $\delta(\hat{p})$, is not a prediction, but instead a decision (where $\delta(x)=1$ if $x>t$ and zero otherwise).

But because $Y $ is binary, shouldn’t a prediction for $ Y $ be binary? Or, another way, an estimate of $p$ is an estimate of a parameter - in this case it is an estimate of the mean of the Bernoulli distribution. An estimate of $p$ is not an estimate of the random variable $Y.$ By definition prediction is a guess at random variable while estimation is a guess at a parameter. So $\hat{p}$ is not a prediction of $Y,$ it is an estimate of $p$, and $\delta(\hat{p})$ is not a classification/decision, it is a prediction.

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    $\begingroup$ Your question might become clearer if accompanied by a definition of what you mean by "prediction," because this term has various meanings in statistics. $\endgroup$ – whuber Jan 20 at 18:38
  • $\begingroup$ @whuber added, it is the definition you gave in the linked answer. $\endgroup$ – user0 Jan 20 at 23:49
  • $\begingroup$ You don't use it in the sense I gave, because in general a parameter is not a probability. $\endgroup$ – whuber Jan 21 at 0:25
  • $\begingroup$ @whuber updated - here I am referring to the Bernoulli case $\endgroup$ – user0 Feb 15 at 0:58
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    $\begingroup$ Thank you. I am reminded of the colloquial sense of prediction, such as what the weatherman announces. When they say "there's a 60% chance of rain tomorrow," isn't that generally understood as a prediction? Of course if someone says "it will rain tomorrow," that's a prediction, too, but it is no longer probabilistic. Accordingly, in statistics a "prediction" is generally thought to be a characterization of an unobserved random variable based on inferred relationships with observed random variables, in contradistinction to a definite statement about a value of the unobserved variable. $\endgroup$ – whuber Feb 15 at 15:23
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Some nomenclature issues here. A classification is a prediction (I predict case x is class A based on the data/model).

But I think what you are getting at is the fact that most implementations of classification in computer software can return a binary classification - but this is simply a categorization based on computed probabilities relative to some threshold. You are free to change that threshold and thus your classification based on those extracted probabilities.

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  • $\begingroup$ The main claim is usually that a classification is binary (or categorical) by definition and that a binary value is not prediction. I am not sure if this is equivalent to saying that a binary value is not a prediction. $\endgroup$ – user0 Jan 20 at 23:53
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    $\begingroup$ @user0 yea the inclusion or exclusion of ‘a’ is key here. $\endgroup$ – HEITZ Jan 20 at 23:56
  • $\begingroup$ @user0 I hear you but you are taking issue with semantics. I don’t think this deserves a downvote. The point I am making is valid $\endgroup$ – HEITZ Mar 13 at 7:20

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