Interpretable Machine Learning: Explainability in Algorithm Design
DOI:
https://doi.org/10.70393/6a69656173.323337ARK:
https://n2t.net/ark:/40704/JIEAS.v2n6a07Disciplines:
Computer ScienceSubjects:
Machine LearningReferences:
22Keywords:
Machine Learning, AutoML, Computational EfficiencyAbstract
In recent years, there is a high demand for transparency and accountability in machine learning models, especially in domains such as healthcare, finance and etc. In this paper, we delve into deep how to make machine learning models more interpretable, with focus on the importance of the explainability of the algorithm design. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML. To that end, we first introduce the AutoML technology and review its various tools and techniques.
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