Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default
Lisa Crosato,
Caterina Liberati,
Marco Repetto
Abstract:Academic research and the financial industry have recently paid great attention to MachineLearning algorithms due to their power to solve complex learning tasks. In the field of firms' default prediction, however, the lack of interpretability has prevented the extensive adoption of the black-box type of models. To overcome this drawback and maintain the high performances of black-boxes, this paper relies on a model-agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors… Show more
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