Ensemble methods take several machine learning techniques and combine them into one predictive model. It is a two step process:
- Generate the Base Learners: Choose any combination of base learners, based on accuracy and diversity. Each base learner can produce more than one predictive model, if you change variables such as case weights, guidance parameters, or input space partitions.
- Combine Estimates from the Base Learners. The result is a computational “average” of sorts (which is much more complex than the regular arithmetic average).
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References
- Lior, R. (2019). Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition). World Scientific.
- Seni, G. & Elder, J. (2010). Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. Morgan & Claypool Publishers.
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