Also known as: independently and identically distributed (i.i.d) Β· IID
IID, or independently and identically distributed, describes data points drawn from the same distribution where each observation is independent of the others. It is a common mathematical assumption in machine learning, although real-world data often violates it because of time, context, user behavior, or sampling effects.
Data drawn from a distribution that doesn't change, and where each value drawn doesn't depend on values that have been drawn previously. An i.i.d. is the ideal gas of machine learning---a useful mathematical construct but almost never exactly found in the real world. For example, the distribution of visitors to a web page may be i.i.d. over a brief window of time; that is, the distribution doesn't change during that brief window and one person's visit is generally independent of another's visit. However, if you expand that window of time, seasonal differences in the web page's visitors may appear. See also nonstationarity.