AI engineering glossary · Classical ML algorithms
What is ensemble learning?
Ensemble learning: Combining the predictions of several models so that the group is more accurate or stable than any single member. Main styles are bagging, boosting and stacking. It works best when members are individually decent yet make different mistakes.
Ensemble learning: an example
Averaging the outputs of a logistic model, a forest and a boosted model to predict loan default.
Related terms
More Classical ML algorithms terms
- Entropy
- Euclidean distance
- Expectation-maximisation
- Explained variance ratio
- Extremely randomised trees
- Feature importance
- Gaussian mixture model
- Gaussian naive Bayes
Where you learn it
Ensemble learning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.