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Independent and Identically Distributed (IID) Assumption
In standard supervised learning, we commonly rely on the independent and identically distributed (IID) assumption. This principle dictates that both the training and test data are drawn independently from the exact same underlying probability distribution, denoted as . Without this strong assumption that (where is the test distribution), it would be impossible to justify using patterns learned from the training data to make predictions on new test data.
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Independent and Identically Distributed (IID) Assumption