Ensemble methods take several machine learning techniques and combine them into one predictive model. It is a two step process: 

  1. 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.
  2. Combine Estimates from the Base Learners. The result is a computational "average" of sorts (which is much more complex than the regular arithmetic average).

Click on picture to zoom in


  • Lior, R.  (2019). Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition). World Scientific.
  • Seni, G. & Elder, J. (2010). Ensemble Methods in Data MiningImproving Accuracy Through Combining Predictions.  Morgan & Claypool Publishers.

Views: 4897


You need to be a member of Data Science Central to add comments!

Join Data Science Central

© 2021   TechTarget, Inc.   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service