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Ensemble Methods

Ensemble MethodsBy Zhi-Hua Zhou
About Ensemble Methods

Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

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  • Language:
  • English
  • ISBN:
  • 9781032960609
  • Binding:
  • Hardback
  • Pages:
  • 348
  • Published:
  • March 8, 2025
  • Edition:
  • 2
  • Dimensions:
  • 242x163x27 mm.
  • Weight:
  • 700 g.
  In stock
Delivery: 3-5 business days
Expected delivery: April 9, 2025

Description of Ensemble Methods

Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

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