While understanding and trusting models and their results is a hallmark of good (data) science, model interpretability is a serious legal mandate in the regulated verticals of banking, insurance, and other industries. Moreover, scientists, physicians, researchers, and humans in general have the right to understand and trust the models and modeling results that affect their work and their lives. Today, many are embracing deep learning and machine learning techniques, but what happens when people want to explain these impactful, complex technologies or when these technologies inevitably make mistakes?
Patrick Hall and Sri Satish share several approaches beyond the error measures and assessment plots typically used to interpret deep learning and machine learning models and results. Wherever possible, interpretability approaches are deconstructed into more basic components suitable for human story telling: complexity, scope, understanding, and trust.
For more information, see Patrick and Sri’s recent article “Ideas on interpreting machine learning” on O’Reilly Ideas.
Patrick Hall is a senior director for data science products at H2o.ai where he focuses mainly on model interpretability. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning.
Previously, Patrick held global customer-facing and R&D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick is the eleventh person worldwide to become a Cloudera Certified Data Scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Sri Satish is cofounder and CEO of H2O.ai, the builders of H2O. H2O democratizes big data science and makes Hadoop do math for better predictions. Previously, Sri spent time scaling R over big data with researchers at Purdue and Stanford; cofounded Platfora; was the director of engineering at DataStax; served as a partner and performance engineer at the Java multicore startup Azul Systems, where he tinkered with the entire ecosystem of enterprise apps at scale; and worked on a NoSQL trie-based index for semistructured data at in-memory index startup RightOrder. Sri is known for his knack for envisioning killer apps in quickly evolving spaces and assembling stellar teams to productize that vision. He is a regular speaker on the big data, NoSQL, and Java circuit and leaves a trail at @srisatish.
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