Regularization and False Alarms Quantification: Two Sides of the Explainability Coin

Regularization is a well-established technique in machine learning (ML) to achieve an optimal bias-variance trade-off which in turn reduces model complexity and enhances explainability. To this end, some hyper-parameters must be tuned, enabling the ML model to accurately fit the unseen data as well...

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Veröffentlicht in:	arXiv.org 2020-12
Hauptverfasser:	Safaei, Nima, Assadi, Pooria
Format:	Artikel
Sprache:	eng
Schlagworte:	Alarms False alarms Machine learning Mathematical models Parameters Regularization
Online-Zugang:	Volltext
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