Unlocking the Secrets of Black Box Models: Interpretability & Explanation Techniques | Anaconda

ABOUT THE TALK:
There has been an increasing interest in machine learning model interpretability and explainability. Researchers and ML practitioners have designed many explanation techniques such as explainable boosting machine, visual analytics, distillation, prototypes, saliency map, counterfactual, feature visualization, LIME, SHAP, interpretML, and TCAV. In this talk, Sophia Yang provides a high-level overview of the popular model explanation techniques.

ABOUT THE SPEAKER:
Sophia Yang is a Senior Data Scientist and a Developer Advocate at Anaconda. She is passionate about the data science community and the Python open-source community. She is the author of multiple Python open-source libraries such as condastats, cranlogs, PyPowerUp, intake-stripe, and intake-salesforce.

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