Hands-On Machine Learning for Algorithmic Trading

Ensemble models

Ensemble learning involves combining several machine learning models into a single new model that aims to make better predictions than any individual model. More specifically, an ensemble integrates the predictions of several base estimators trained using one or more given learning algorithms to reduce the generalization error that these models may produce on their own.

For ensemble learning to achieve this goal, the individual models must be:

• Accurate: They outperform a naive baseline (such as the sample mean or class proportions)
• Independent: Their predictions are generated differently to produce different errors

Ensemble methods are among the most successful machine learning algorithms, in particular for standard numerical ...