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A feature sensitivity and dependency analysis approach for model explainability

Ritsema, S. (2021) A feature sensitivity and dependency analysis approach for model explainability.

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Abstract:The application of machine learning models in multiple fields where data comes into play is increasing. However, for some models, there is no real justification or explanation for the decisions made by the model. This is a so-called black box model. The data simply gets fed into the model, which returns a prediction. This makes it difficult to verify the behaviour and robustness of a model. Several studies have been done on improving model explainability, however there are unexplored areas in this field. This paper looks into a novel approach for gaining insight into a model's robustness: feature sensitivity and dependency analysis. A feature is sensitive when a small change in the feature's value leads to a major change in the predicted outcome. This research defines a strategy to calculate and display feature sensitivity and explores the influence of feature dependency on feature sensitivity. The techniques presented in this paper have shown to give insight in the robustness and the decision making process of machine learning models. This contributes to increasing the interpretability of black box models.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:https://purl.utwente.nl/essays/87063
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