Speaker
Description
Abstract:
When crossing a busy street, we understand that keeping our eyes open isn't optional—it's how we stay safe. Yet when building complex models, we often choose to work blind. Some of this is understandable — visualising high-dimensional data is genuinely difficult. But cultural attitudes matter too: there's a lingering belief that "looking at the data" compromises objectivity, and a tendency to view diagnostics as about as appealing as cleaning house. Yet the landscape is shifting. Visualising high dimensions is getting easier, and explainability is now considered as important as predictive accuracy.
This talk demonstrates how to open our eyes when building models. I'll begin with the Rashomon quartet, showing how visualising the simulated training data reveals why four equally high-performing models yield strikingly different interpretations - an example of visualising fitted models relative to observed data.. I'll then tackle a critical challenge: local model explanations often conflict, and interactive graphics in high dimensions can help determine which explanations are trustworthy. Finally, I'll outline practical ways to integrate these visual methods into the tidymodels workflow, making visualisation a natural part of model development.
Bio:
Dianne Cook is a Professor of Statistics in Econometrics and Business Statistics at Monash University in Melbourne, Australia. She holds a PhD in Statistics from Rutgers University. Her research focuses on statistical graphics, with an emphasis on interactive visualisation of high-dimensional data and statistical inference for data visualisation. Di is a Fellow of the American Statistical Association, elected member of the International Statistical Institute, and a Board Member of the R Foundation. She is a past editor of the Journal of Computational and Graphical Statistics, and The R Journal, and author of numerous R packages. She is actively involved in R Ladies Melbourne, the Statistical Computing and Visualisation Section of the Statistical Society of Australia, and the Graphics and Computing Sections of the American Statistical Association.