To verify how well statistical learning machines have learned a phenomenon, a step called model evaluation is carried out. This aims to evaluate whether the model (the mathematical abstraction learned from specific instances) can generalize well.

## Overfitting

A model may not generalize well if it overfits the data [1], which occurs when

the model becomes sensitive to the noise in the data set. Below, we illustrate

on a concrete example the concept of model overfitting.

## Learning word-by-word and not the general concepts

A practical example of overfitting would be that of a student who tries very

hard to learn every words in a textbook by heart but who fails to pick up the

general concepts taught by the textbook.

## Reference

- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001.
- Yeom H, Kim J, Chung C Creative Commons Attribution 4.0 International