Professional paper

https://doi.org/10.19279/TVZ.PD.2023-11-4-04

ENSEMBLE METHODS OF MACHINE LEARNING

Aleksandar , Stojanović ; Zagreb University of Applied Sciences, Vrbik 8, 10000 Zagreb, Croatia
Željko Kovačević ; Zagreb University of Applied Sciences, Vrbik 8, 10000 Zagreb, Croatia
Danko Ivošević ; Zagreb University of Applied Sciences, Vrbik 8, 10000 Zagreb, Croatia

Abstract

Ensemble machine learning methods have attracted considerable attention in recent years due to their ability to improve the accuracy and robustness of predictive models. These methods combine the results of multiple individual models to produce a final prediction. Ensemble methods are more resistant to data deviations. They can be applied to a wide range of problems in the field of machine learning, including classification, regression, and clustering. They can generally help improve the performance of machine learning models and are widely used in practice. Due to their great importance and significance, this article provides an overview of some of the most used ensemble methods in machine learning, including bagging, boosting, and stacking, and describes the advantages and limitations of each of these approaches.

Keywords

ensemble; machine learning; bagging; boosting; stacking

Hrčak ID:

318105

URI

https://hrcak.srce.hr/318105

Publication date:

5.2.2024.

Article data in other languages: croatian

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