Introduction to Adversarial Validation In Machine Learning.
There are many sources of overfitting, but an important one is when your training and test data do not come from the same distribution.
Unfortunately, this is not an uncommon problem. For example, training a model with data collected during a period different from the test or production data could lead to poor performance. Even slight differences could considerably affect your results, but this is still an issue many people struggle to identify and decide how to better move forward.
That's where Adversarial Validation comes in.
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