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Overfitting

698 bytes added, 20:14, 24 May 2014
Remedies for overfitting
===Separate training examples and test examples===
One of the standard practices to detect and correct for the problem of overfitting in machine learning is to have separate sets of training examples and test examples. The training examples are used to learn and form the internal model for how to solve the problemsproblem, and the test examples are then used to test whether the model is correct. The key idea is that test examples are ''withheld'' during the training process, so as to allow them to play the role of independently assessing the model one has learned. If performance on the test examples is significantly worse than performance on the training examples, this suggests that the model one used to learn is overfit to the training examples. If this happens, then adjustments need to be made to the model. Note that the term ''test'' here does not (necessarily) refer to the formal assessments given by instructors. Rather, it may refer to a set of practice examples that the student earmarks for later use.
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