concept ensemble method in category machine learning

This is an excerpt from Manning's book Grokking Machine Learning MEAP V09. Login to get full access to this book.

Figure 10.1. Ensemble methods consist of joining several weak learners in order to build a strong learner.

10.6       Applications of ensemble methods

Ensemble methods are some of the most useful machine learning techniques used nowadays as they exhibit great levels of performance with relatively low cost. One of the places where ensemble methods are used the most is in machine learning challenges such as the Netflix challenge. The Netflix challenge was a competition that Netflix organized, where they anonymized some data and made it public. The competitors’ goal was to build a better recommendation system than Netflix itself; the best system would win one million dollars. The winning team used a very strong combination of weak learners in an ensemble to win.

This is an excerpt from Manning's book Ensemble Methods for Machine Learning MEAP V01. Login to get full access to this book.

An ensemble method is a machine-learning algorithm that aims to improve predictive performance on a task by aggregating the predictions of multiple estimators or models. In this manner, an ensemble method learns a meta-estimator.

The key to success with ensemble methods is ensemble diversity. Informally, ensemble diversity refers to the fact that individual ensemble components, or machine-learning models, are different from each other.