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Abstract: Visual representations of data inform how machine learning practitioners think, understand, & decide. Before charts are ever used for outward communication about a ML system, they are used by the system designers & operators themselves as a tool to make better modeling choices. Practitioners use visualization, from very familiar statistical graphics to creative & less standard plots, at the points of most important human decisions when validation of those decisions can be difficult. Visualization approaches are used to understand both the data that serves as input for machine learning & the models that practitioners create. In this talk, learn about the process of building a ML model in the real world, how & when practitioners use visualization to make more effective choices, & considerations for ML visualization tooling.
Bio: Julia Silge is a data scientist & software engineer at RStudio PBC where she works on open source modeling tools. She is both an international keynote speaker & a real-world practitioner focusing on data analysis & machine learning practice. She is the author of Text Mining with R with her coauthor David Robinson & Supervised Machine Learning for Text Analysis in R with her coauthor Emil Hvitfeldt. She loves text analysis, making beautiful charts, & communicating about technical topics with diverse audiences.