Desktop Survival Guide
by Graham Williams


Usage: Classification tasks, regression and other modelling.
Input: Training data consisting of entities expressed as attribute-value pairs, with a class associated with each observation.
Output: An ensemble of models which are to be deployed together with their decisions being combined to give a joint decision.
Complexity: Depends on complexity of the weak learner employed, but generally the weak learner is quite simple (e.g., OneR or Decision Stumps) hence scalability is generally good.
Availability: Freely available in Weka (See Chapter 53) and in R (See Chapter 50). Commercial data mining toolkits implementing AdaBoost include TreeNet (See Chapter 61), Statistica (See Chapter 60), and Virtual Predict (See Chapter 62).

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