10-601 Ensembles 1

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This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014




You should know how to implement these ensemble methods, and what their relative advantages and disadvantages are:

  • (Ziv - not sure if can do in one lecture if we do boosting) Bagging
  • Boosting
  • Stacking
  • Multilevel Stacking
  • (Ziv - not sure if I will do this) The "bucket of models" classifier
  • Random forest