Ensemble classifiers

Thomas G Dietterich defines Ensemble methods as follows:

"Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their prediction."

You can get more information from http://web.engr.oregonstate.edu/~tgd/publications/mcs-ensembles.pdf.

Ensemble methods create a set of weak classifiers and combine them into a strong classifier. A weak classifier is a classifier that performs slightly better than a classifier that randomly guesses the prediction. Rattle offers two types of ensemble models: Random Forest and Boosting.


Boosting is an ensemble method, so it creates a set of different classifiers. Imagine that you have m classifiers, we can ...

Get Qlik Sense: Advanced Data Visualization for Your Organization now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.