Table of Contents

Data Mining - Ensemble Learning (meta set)

About

Combining multiple models into ensemble in order to produce an ensemble for learning.

(Committee| Collective Intelligence) decision of different classifier algorithms.

Having different classifiers (also known as expert, base learners) with different perspectives and let them vote is often a very effective and robust way of making good decisions.

Diversity help, especially when the learners (model) are unstable (when small changes in the training data can produce large changes in the learned model).

Create diversity by

Advantage / Disadvantage

The output is hard to analyze but the performance is really good.

Documentation / Reference