Dr. Rebecca Bilbro

Dr. Rebecca Bilbro is a data scientist, Python and Go programmer, teacher, speaker, and author in Washington, DC. She specializes in visual diagnostics for machine learning, from feature analysis to model selection and hyperparameter tuning, and has conducted research on natural language processing, semantic network extraction, entity resolution, and high dimensional information visualization. An active contributor to the open source software community, Rebecca enjoys collaborating with other developers on inclusive projects like Scikit-Yellowbrick - a pure Python visualization package for machine learning that extends scikit-learn and Matplotlib to support model selection and diagnostics. In her spare time, she can often be found either out-of-doors riding bicycles with her family or inside practicing the ukulele. Rebecca earned her doctorate from the University of Illinois, Urbana-Champaign, where her research centered on communication and visualization in engineering.

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Institute / Company – ICX Media, Inc Twitter handle – @rebeccabilbro Homepage – https://rebeccabilbro.github.io/ Git*hub|lab – https://github.com/rebeccabilbro


Visual Diagnostics at Scale

Machine learning is a search for the best combination of features, model, and hyperparameters. But as data grow, so does the search space! Fortunately, visual diagnostics can focus our search and allow us to steer modeling purposefully, and at scale.