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overfitting models to data can be a real pain to diagnose and to fix sometimes its obvious if your model is performing far better on the training set than the holdout set then its pretty clear you have a problem but other times it can be more subtle you might expect some degradation in performance between training and tests but how much is too much and its not enough to just know the ear overfitting its helpful to know which variables are causing the problem this is where adversarial validation can help its a fancy sounding term but its really a pretty simple technique that allows you to not only get some measure of your overfitting but also highlight which variables are responsible its used pretty extensively in cattle competitions to help identify these sources of overfitting which are then corrected for additional performance gains its also an effective test of perhaps the most pervasive assumption and all machine learning which is that your data are independent and identica
