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I have been seeing this term used in a few books in machine learning, however, the authors never care to explain what that actually means, they just use it. I hope to understand this concept as it seems fairly easy, but I don't want to have a wrong idea about it.

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Please add some context in which you find this term, sometimes terms explain themselves out of context.

Without further context my understanding is, that machine learning models are trained on a sample of data and that extra sample or out-of-sample means, that this observation was not used in the training phase of the model.

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