Pramit will talk in detail about enabling explainable AI, including: the need for better model interpretation, how to build interpretable machine learning system using the open source framework “Skater”, and Bayesian Rule Lists, and other algorithms for assisting human decision.
Pramit Choudhary is a Lead data scientist/ML scientist at h2o.ai, where he focuses on optimizing and applying classical machine learning and Bayesian design strategy to solve large scale real-world problems.
Currently, he is leading initiatives on figuring out better ways to generate a predictive model’s learned decision policies as meaningful insights(Supervised/Unsupervised problems)
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