What can Borges teach you about overfitting?

What can Borges teach you about overfitting?

Jorge Luis Borges (1899–1986) was a brilliant writer and one of the most influential authors of all times. But what can Borges teach you about overfitting?

Jorge Luis Borges (1899–1986) was a brilliant writer and one of the most influential authors of all times. But what can Borges teach you about overfitting?

Funes the memorious

Borges wrote Funes the memorious (originally “Funes el memorioso”) in 1954. This tale was born after the author suffered from insomnia. This tale tells the story of Ireneo Funes who suffers from hypermnesia. After a horseback riding accident, Funes discovers that he can remember absolutely everything: the shape of the clouds in every moment of the day, the position of the light in every corner of the house, what he did minute by minute two months ago, etc.

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Jorge Luis Borges image under public domain.

In this tale, Borges explores various topics regarding several aspects of our life that require the “art of forgetting”. By remembering absolutely everything Funes loses one of the most important features of the thinking process: generalization. Funes cannot understand how the term “dog” can group every dog if they are clearly different. He can easily differentiate the small black dog with shiny eyes from the small black dog with that red dot in the left eye, but he cannot understand what makes a dog to be a dog.

Overfitting or the lost of generalization

Funes hypermnesia is more a misfortune than a gift. With no generalization is impossible to use abstract thinking. And without abstract thinking, Funes is closer to a machine rather than a human being. He goes into the opposite direction of what we expect to obtain with machine learning.

Overfitting is to machine learning what hypermnesia is to Funes. Overfitted models cannot distinguish between noisy observations and the underlying model. This is, they cannot generalize.

The figure below shows two binary classifiers (black and green lines). The overfitted classifier (green line) is very dependant on the training data and it is very likely to have a poor performance when new observations arrive.

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