When used incorrectly, the risk of machine learning (ML) overfitting is extremely high. However, ML counts with sophisticated methods to prevent: (a) train set overfitting, and (b) test set overfitting.
Thus, the popular belief that ML overfits is false. A more accurate statement would be that: (1) in the wrong hands, ML overfits, and (2) in the right hands, ML is more robust to overfitting than classical methods.
When it comes to modelling unstructured data, ML is the only choice. Classical statistics should be taught as a preparation for ML courses, with a focus on overfitting prevention.
Fonte: López de Prado, Marcos, Overfitting: Causes and Solutions (Seminar Slides) (February 26, 2020). Available at SSRN: https://ssrn.com/abstract=3544431 or http://dx.doi.org/10.2139/ssrn.3544431
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