Ensuring high quality of a vehicle will increase the lifetime and customer experience, in addition to the maintenance problems, and it is important that there are objective scientific methods available, for evaluating the quality of the vehicle. In this paper, we present a computational framework for evaluating the vehicle quality based on interpretable machine learning techniques. The validation of the proposed framework for a publicly available vehicle quality evaluation dataset has shown an objective machine learning based approach with improved interpretability and deep insight, by using several post-hoc model interpretability enhancement techniques.
Keywords: Explainability; Interpretability; Machine learning; Risk assessment; Vehicle quality.
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