Overfitting reading list

Overfitting is a situation in which a model accurately describes some data but not the phenomenon that generates that data. Overfitting was a huge problem in the good old times, where each data point was expensive, and researchers operated on datasets that could fit a single A4 sheet of paper. Today, with mega- giga- and tera-bytes datasets, overfitting is … still a problem. A very painful one. Following is a short reading list on overfitting.

I would like to start with Mehmet Suzen mllib.wordpress.com who treats overfitting as “inaccurate meme in supervised learning

cross-validation does not prevent your model to overfit and good out-of-sample performance does not guarantee not-overfitted model.

Another blogger, whose name I couldn’t find, has two very detailed posts on overfitting:

Understanding overfitting from bias-variance trade-off and Understanding overfitting from Haussler 1988 theorem

Finally, Adrian from the “morning paper” (please don’t tell me you don’t follow that blog) has a summary of another paper, titled “Understanding deep learning requires re-thinking generalization” (I only read Adrian’s summary).


No conclusions here. It’s a reading list.

Featured image credit: https://en.wikipedia.org/wiki/Overfitting#/media/File:Overfitting.svg

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