Ensembling (sometimes ensemble learning) is a class of meta-algorithmic techniques where multiple models are trained and their results are aggregated to improve classification performance. It is effective in a wide variety of problems. 

Two commonly used methods are: 

  • boosting: a method of weighting the predictions of multiple models such that the combined prediction is more accurate than each individual model
  • bagging: also known as bootstrap aggregating, where multiple models are trained on random subsets of the entire training dataset, and the results of these models are averaged
Artificial intelligence

Article information

rID: 56228
Synonyms or Alternate Spellings:
  • Ensemble Learning

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