Data Leaders Brief

White-Box vs Black-Box Models: Balancing Interpretability and Accuracy

Dataiku

JANUARY 31, 2020

Data scientists and business leaders building or using machine learning models and AI systems face a serious challenge today -- how to balance interpretability and accuracy stemming from the difference between black-box and white-box models.

Modeling

Modeling Machine Learning

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

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

Data Leaders Brief

White-Box vs Black-Box Models: Balancing Interpretability and Accuracy

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

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