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Wednesday, June 26 • 14:00 - 14:20
Game Theory and Model Interpretability

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Many machine learning models are (in)famous for being black boxes. However, model explainability is a hot topic and new techniques have been brought to life in order to overcome that. This talk will walk through how a 60+ year-old game theory concept has been adapted so that we can interpret machine learning models regardless of their inner structure and complexity — yes, neural nets and ensembles included. Most importantly, we'll talk about how you can apply this technique today in order to build better models and come up with more useful data products.

avatar for Gabriel Cypriano Saca

Gabriel Cypriano Saca

Data Scientist, Grupo ZAP
Gabriel is currently a Data Scientist at Grupo ZAP and a Data Science Instructor at Tera. He has previously worked as a Data Scientist at Creditas and at K2 Data Science. Gabriel discovered the world of Machine Learning while co-founding Songwich, a music recommendation startup.

Wednesday June 26, 2019 14:00 - 14:20 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil