What is adversarial validation and why is it needed?

Adversarial validation is a method to check how similar the distributions of the training and test samples are. For this, a classifier is created whose task is to distinguish objects from the training sample from objects in the test sample. If the classifier shows high accuracy, it means the distributions differ significantly, and the model may generalize poorly to the test data.

Why is this needed:

• To check if there is bias between the training and test samples.
• To understand how correctly the test set is chosen.
• In tasks with tabular data and boosting models, it helps identify problems with generalization.

Example: if adversarial validation shows that data from different samples are easily separable, it is worth reconsidering the data splitting strategy or applying adaptation methods.