Calibrate: Interactive Analysis of Probabilistic Model Output

Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Claudio Silva

View presentation: 2022-10-20T16:33:00Z GMT-0600 Change your timezone on the schedule page
2022-10-20T16:33:00Z
Exemplar figure, described by caption below
Calibrate allows users to interactively analyze model calibration in Jupyter Notebooks. The system allows for easy creation of conventional and learned reliability diagrams. The reliability diagrams are coupled with a prediction histogram to provide better context for the users. The diagrams also allow for brushing to analyze prediction regions. When a selection is made, the instance view, shown below the reliability diagram, is updated. Users can also create and analyze subsets of the data by brushing on feature distributions.

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Abstract

Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports betting, or patient risk prediction, rely on a classifier's predicted probabilities rather than predicted labels. In these instances, practitioners are concerned with producing a calibrated model, that is, one which outputs probabilities that reflect those of the true distribution. Model calibration is often analyzed visually, through static reliability diagrams, however, the traditional calibration visualization may suffer from a variety of drawbacks due to the strong aggregations it necessitates. Furthermore, count-based approaches are unable to sufficiently analyze model calibration. We present Calibrate, an interactive reliability diagram that addresses the aforementioned issues. Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection. We demonstrate the utility of Calibrate through use cases on both real-world and synthetic data. We further validate Calibrate by presenting the results of a think-aloud experiment with data scientists who routinely analyze model calibration.