Further Exploration into Model Explainability with H2O Driverless AI 1.9

Presented by

Benjamin Cox, Director of Product Marketing at H2O.ai

About this talk

With the latest release of H2O Driverless AI (1.9.0), we have added a litany of new features to enhance the user experience and empower companies to build models in the most responsible and transparent manner. With the addition of multiple fairness metrics such as, Disparate Impact Analysis, and leading edge explainable modeling methods such as Explainable Neural Networks (XNN) and GA2M, Driverless AI users are equipped to further explore model explainability techniques within the platform. In this webinar, you will learn about: - Disparate Impact Analysis and Standard Mean Difference - Exporting Decision tree model rules as txt & kernel explainer for Shapley Values - XNNs & GA2M Presenter: Benjamin Cox, Director of Product Marketing at H2O.ai
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H2O.ai is the maker of H2O, the world's best machine learning platform and Driverless AI, which automates machine learning. H2O is used by over 200,000 data scientists and more than 18,000 organizations globally. H2O Driverless AI does auto feature engineering and can achieve 40x speed-ups on GPUs.