PAPIs Latam 2019

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.