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Ensembling


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In Machine learning, ensembling is taking multiple models and combining their predictions into a single prediction with better accuracy.

There are different ways of doing this.

Averaging

The simplest one is averaging. This can be used for regression tasks.

Let’s say you have two models. The prediction of the first model is $P_1$ and the prediction of the second model is $P_2$. Then the prediction of their ensemble can be $\frac{P_1 + P_2}{2}$.

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Citation

If you find this work useful, please cite it as:
@article{yaltirakli,
  title   = "Ensembling",
  author  = "Yaltirakli, Gokberk",
  journal = "gkbrk.com",
  year    = "2025",
  url     = "https://www.gkbrk.com/ensembling"
}
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IEEE Citation
Gokberk Yaltirakli, "Ensembling", August, 2025. [Online]. Available: https://www.gkbrk.com/ensembling. [Accessed Aug. 16, 2025].
APA Style
Yaltirakli, G. (2025, August 16). Ensembling. https://www.gkbrk.com/ensembling
Bluebook Style
Gokberk Yaltirakli, Ensembling, GKBRK.COM (Aug. 16, 2025), https://www.gkbrk.com/ensembling

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