Conformal prediction and its integration within visual analytics toolbox
Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 152:286-293, 2021.
Conformal prediction is a machine learning approach to report on the reliability of predictive models when applied to new cases. Machine learning techniques are gaining in complexity, and assessing their reliability may be an essential part of explaining the inner workings of predictive models. For practical purposes and dissemination of conformal prediction techniques, we must include these within easily accessible toolboxes. In machine learning, a significant subset of such toolboxes is those that use work flows and visual programming. Here, we report on an example of such a toolbox, Python implementation of conformal prediction library, and our initial efforts and ideas to democratize conformal prediction.