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Activity Number: 79
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Graphics
Abstract #311588
Title: Glassbox: An R Package for Visualizing Algorithmic Models
Author(s): Max Ghenis*+ and Ben Ogorek and Estevan Flores
Companies: Google and Google and Google
Keywords: R ; Random forests ; Data visualization ; Prediction ; ggplot2 ; Machine learning

Black-box prediction models like random forests often strike an ideal balance between robustness, accuracy, and ease of use for analysts. In a business setting, however, the difficulty of interpretation is a drawback.

We propose a novel approach for visualizing such models using model predictions across predictors' observed ranges. By smoothing the predicted trend of the test set's response curves, we detect meaningful relationships which would otherwise remain masked.

We introduce the Glassbox R package which implements these techniques for any prediction model, and describe examples of Glassbox's usage from our studies in Google's People Operations department.

Authors who are presenting talks have a * after their name.

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