Hands-On Machine Learning for Algorithmic Trading

How to hedge against overfitting

One popular technique to control overfitting is that of regularization, which involves the addition of a penalty term to the error function to discourage the coefficients from reaching large values. In other words, size constraints on the coefficients can alleviate the resultant potentially negative impact on out-of-sample predictions. We will encounter regularization methods for all models since overfitting is such a pervasive problem.

In this section, we will introduce shrinkage methods that address two motivations to improve on the approaches to linear models discussed so far:

• Prediction accuracy: The low bias but high variance of least squares estimates suggests that the generalization error could be ...