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In the machine learning world, offline learning refers to situations where the program is not operating and taking in new information in real time. Instead, it has a static set of input data. The opposite is online learning, where the machine learning program is working in real time on data that comes in.
Offline learning is sometimes described as a proactive type of learning that can work forward on the basis of evaluating the static data sets that it has at its disposal. Because there is no continual influx of information, the program and its human operators can benchmark the results of the training set and apply them to future phases of operation. Some experts call offline learning a form of eager learning, which means that the system is working proactively to make decisions, as compared to lazy learning where the machine learning program's work may be event driven.