Beginner · Data
Ensemble learning
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Combining multiple machine learning models to improve overall prediction accuracy and robustness.
Technical Definition
Combining multiple machine learning models to improve overall prediction accuracy and robustness.
How it works
Ensemble learning involves training several base machine learning models and aggregating their predictions to achieve better performance than any single model could provide. Common techniques include bagging, boosting, and stacking. This approach helps to reduce variance and improve the generalization ability of the combined model.
Related Concepts
- Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.
- Boosting — A machine learning technique that trains models sequentially, with each new model correcting the errors of the previous ones, primarily to reduce bias.
- Bagging — An ensemble technique where models are trained on random subsets of data sampled with replacement to reduce variance.