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7. Ensemble Methods

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Karol Przystalski
Maciej J. Ogorzałek
Jan K. Argasiński
Wiesław Chmielnicki
Published in
Pattern Recognition Primer
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the world of ensemble methods, a powerful approach in machine learning that combines multiple classifiers to enhance performance. The focus areas include boosting, bagging, and stacking, each with its unique techniques and applications. Boosting methods like AdaBoost, Arcing, and RegionBoost are explored, highlighting their sequential building of classifiers and emphasis on misclassified examples. Bagging, or bootstrap aggregation, is exemplified through random forests, demonstrating how multiple models trained on different data subsets can improve accuracy. The chapter also covers stacking, which involves combining different classifiers and using a meta-classifier for final predictions. Practical implementations using Python and scikit-learn are provided, including code snippets for training models, calculating errors, and adjusting weights. Real-world examples, such as wine and breast cancer classification, illustrate the effectiveness of these methods. The chapter concludes with a comparison of ensemble methods against single classifiers, showcasing the superior performance achieved through combining multiple models.
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previous chapter Support Vector Machine
next chapter Neural Networks
Title
Ensemble Methods
Authors
Karol Przystalski
Maciej J. Ogorzałek
Jan K. Argasiński
Wiesław Chmielnicki
Copyright Year
2026
Book
Pattern Recognition Primer

Print ISBN: 978-3-031-91815-5

Electronic ISBN: 978-3-031-91816-2

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-031-91816-2_7
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