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Thread: InterpretML, open-source software toolkit for explaining black box AI, Microsoft Corporation, Redmond, Washington, USA

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    InterpretML, open-source software toolkit for explaining black box AI, Microsoft Corporation, Redmond, Washington, USA


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    Article "InterpretML: A Unified Framework for Machine Learning Interpretability"

    by Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
    September 19, 2019

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    How to explain models with IntepretML Deep Dive

    May 16, 2020

    With the recent popularity of machine learning algorithms such as neural networks and ensemble methods, etc., machine learning models become more like a 'black box', harder to understand and interpret. To gain the stakeholders' trust, there is a strong need to develop tools and methodologies to help the user to understand and explain how predictions are made. In this video, you learn about our open source Machine Learning Interpretability toolkit, InterpretML, which incorporates the cutting-edge technologies developed by Microsoft and leverages proven third-party libraries. InterpretML introduces a state-of-the-art glass box model (EBM), and provides an easy access to a variety of other glass box models and blackbox explainers.

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    The science behind InterpretML: explainable boosting machine

    May 16, 2020

    Learn more about the research that powers InterpretML from Explainable Boosting Machine creator, Rich Caurana from Microsoft Research

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