Ensemble Methods Overview

Ensemble Methods Overview

Ensemble methods combine several models, called base learners, into one stronger predictor. The intuition is that diverse models make different errors, and averaging or voting cancels some of those errors out.

Why ensembles work

• A single model has limited perspective. Many diverse models capture more of the truth together.
• Combining predictions tends to reduce variance and sometimes bias.
• Diversity is essential. Identical models add nothing, so we encourage variety through different data samples, features, or algorithms.

The main families

• Bagging trains models on different bootstrap samples and averages them to cut variance.
• Boosting trains models in sequence, each fixing the previous one's mistakes, to cut bias.
• Stacking trains a meta model to combine the base predictions intelligently.

Tradeoffs

• Ensembles usually improve accuracy but cost more compute and are harder to interpret.
• They are workhorses for tabular data, where they frequently top leaderboards.

Key idea

Ensembles combine diverse base learners whose differing errors partly cancel, with bagging, boosting, and stacking offering distinct ways to build a stronger combined predictor.