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iid is an acronym for independent and identically distributed. Many statistical methods assume that the data are iid; that is, that each observation comes from the same distribution and is independent of other observations.
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Statistical learning when observations are not iid
As far as I am concerned, statistical/machine learning algorithms always suppose that data are independent and identically distributed ($iid$). … Were the data $iid$, I could easily fit a random forest (or any other supervised algorithm, for what matters), thus estimating the conditional expectation of $Y$ given $X$. …