Intermediate · Fundamentals
Ensemble
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Combining multiple independently trained models to improve overall prediction accuracy and robustness.
Technical Definition
Combining multiple independently trained models to improve overall prediction accuracy and robustness.
How it works
An ensemble is a machine learning technique that combines the predictions from several individual models to produce a better overall prediction. The idea is that by aggregating diverse model outputs, the ensemble can reduce variance, bias, and improve accuracy compared to any single model. Common ensemble methods include random forests and gradient boosting.
Related Concepts
- Gradient boosting — A machine learning method that builds models sequentially, with each new model correcting the errors of the previous ones.
- Random forest — An ensemble machine learning method that builds multiple decision trees to improve prediction accuracy and control overfitting.