2016, Knowledge Discovery, Risk and Testing

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

AUGUST 1, 2021

2016) for an example of this technique (LIME). This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. After forming the X and y variables, we split the data into training and test sets. Toy example to present intuition for LIME from Ribeiro (2016).

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Modeling Deep Learning Machine Learning Knowledge Discovery

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Explaining black-box models using attribute importance, PDPs, and LIME

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