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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

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