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