Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning? DOI Creative Commons
María Luz Gámiz, Fernando Jesus Navas-Gomez, Rafael Nozal‐Cañadas

et al.

Machines, Journal Year: 2024, Volume and Issue: 12(12), P. 909 - 909

Published: Dec. 11, 2024

Studying the reliability of complex systems using machine learning techniques involves facing a series technical and practical challenges, ranging from intrinsic nature system data to difficulties in modeling effectively deploying models real-world scenarios. This study compares effectiveness classical statistical methods for improving analysis assessments. Our goal is show that many applications, traditional algorithms frequently produce more accurate interpretable results compared with black-box methods. The evaluation conducted both simulated We report obtained algorithms, as well including neural networks, K-nearest neighbors, random forests.

Language: Английский

Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning? DOI Creative Commons
María Luz Gámiz, Fernando Jesus Navas-Gomez, Rafael Nozal‐Cañadas

et al.

Machines, Journal Year: 2024, Volume and Issue: 12(12), P. 909 - 909

Published: Dec. 11, 2024

Studying the reliability of complex systems using machine learning techniques involves facing a series technical and practical challenges, ranging from intrinsic nature system data to difficulties in modeling effectively deploying models real-world scenarios. This study compares effectiveness classical statistical methods for improving analysis assessments. Our goal is show that many applications, traditional algorithms frequently produce more accurate interpretable results compared with black-box methods. The evaluation conducted both simulated We report obtained algorithms, as well including neural networks, K-nearest neighbors, random forests.

Language: Английский

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