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- W4295036554 abstract "Being able to explain the predictions generated by Machine Learning models is becoming increasingly necessary for numerous applications. It not only validates that the model behaves as expected but also allows for detailed analyses of the results that generate insights about the input data and the application itself. Despite its popularity in the academia, the adoption of such methods, and artificial intelligence itself, in the manufacturing industry is still in its infancy, mostly due to lack of professional expertise and the usage of antiquated technology. In this paper, we train a regression model that predicts the yield value of seamless steel tubes at the end of the heat treatment process. We then use Tree SHAP, a state-of-the-art model explainability framework, to analyze our model. We perform a detailed analysis of several explainability plots that uncovers non-trivial knowledge regarding the heat treatment process and the model itself. Our use case highlights the gains of machine learning explainability analysis in industrial applications, which we hope encourages the adoption of this type of technology in the industry." @default.
- W4295036554 created "2022-09-09" @default.
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- W4295036554 date "2022-07-20" @default.
- W4295036554 modified "2023-09-27" @default.
- W4295036554 title "Explainability Analysis of a Machine Learning Model for Industrial Applications" @default.
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- W4295036554 doi "https://doi.org/10.1109/icecet55527.2022.9872890" @default.
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