Thresholdout: Down with Overfitting
Linear Digressions - Un pódcast de Ben Jaffe and Katie Malone
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Overfitting to your training data can be avoided by evaluating your machine learning algorithm on a holdout test dataset, but what about overfitting to the test data? Turns out it can be done, easily, and you have to be very careful to avoid it. But an algorithm from the field of privacy research shows promise for keeping your test data safe from accidental overfitting
