Guide To Adversarial Validation To Reduce Overfitting in Machine Learning

In this article, we will focus on understanding how to overcome overfitting with adversarial validation and implement this on a sample dataset. Adversarial validation is a technique applied to the data to help reduce overfitting in cases where the overfitting problem is subtle.

Overfitting a model to your data is one of the most common challenges you will face as a Data Scientist. This problem may be obvious sometimes, like when the model performs incredibly well on training data but poorly on the test data. When this happens, you know the model has overfitted and will try and fix it with cross-validation or hyperparameter tuning. But sometimes the problem of overfitting is very subtle, and not easily noticed.

Consider a situation where the test and train data are not obtained from the same source. There are chances that the patterns in the dataset differ. In such cases performing cross-validation will not help in solving the overfitting problem because the data for validation comes from the training set. To overcome these problems, adversarial validation is used.

In this article, we will focus on understanding how to overcome overfitting with adversarial validation and implement this on a sample dataset.

machine learning

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