Bachelor ThesisExplainable Failure Analysis in Time-Series Data Using SHAP
Explainability is critical for the adoption of machine learning models in industrial systems, particularly for failure detection and prediction. This thesis explores the application of SHapley Additive exPlanations (SHAP) to time-series data containing failure events, focusing on attributing model decisions to both relevant features and critical time steps to improve transparency and trust. Requirements Please […]Explainability is critical for the adoption of machine learning models in industrial systems, particularly for failure detection and prediction. This thesis explores the application of SHapley Additive exPlanations (SHAP) to time-series data containing failure events, focusing on attributing model decisions to both relevant features and critical time steps to improve transparency and trust. Requirements Please […]
