The paper develops a transparent data-driven fault detection system for manufacturing that integrates supervised ML classification, SHAP explanations, and operator-focused visualizations, reporting 95.9% accuracy on univariate crimping time series data.
API design for machine learning software: experiences from the scikit-learn project
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abstract
Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.
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UNVERDICTED 2representative citing papers
A software framework integrates heterogeneous causal inference, policy learning, mediation, forecasts, variance reduction, and anytime-valid inference into one AI-orchestratable interface for business experimentation.
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Towards transparent and data-driven fault detection in manufacturing: A case study on univariate, discrete time series
The paper develops a transparent data-driven fault detection system for manufacturing that integrates supervised ML classification, SHAP explanations, and operator-focused visualizations, reporting 95.9% accuracy on univariate crimping time series data.
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Closing the Loop: A Software Framework for AI to Support Business Decision Making
A software framework integrates heterogeneous causal inference, policy learning, mediation, forecasts, variance reduction, and anytime-valid inference into one AI-orchestratable interface for business experimentation.