How to interpret and explain your black-box models?

Sophia Yang, Senior Data Scientist | Anaconda

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, we will provide a high-level overview of the popular model explanation techniques.


Sophia Yang

Senior Data Scientist | Anaconda

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. She serves on the Steering Committee and the Code of Conduct Committee of the Python open-source visualization system HoloViz. She also volunteers at NumFOCUS, PyData, and SciPy conferences. She holds an M.S. in Computer Science, an M.S. in Statistics, and a Ph.D. in Educational Psychology from The University of Texas at Austin.

Sophia Yang