Conformal Prediction with Orange

Conformal Prediction with Orange

conformal prediction, nonconformity, machine learning, Python, Orange

Conformal predictors estimate the reliability of outcomes made by supervised machine
learning models. Instead of a point value, conformal prediction defines an outcome region
that meets a user-specified reliability threshold. Provided that the data are independently and identically distributed, the user can control the level of the prediction errors
and adjust it following the requirements of a given application. The quality of conformal
predictions often depends on the choice of nonconformity estimate for a given machine
learning method. To promote the selection of a successful approach, we have developed
Orange3-Conformal, a Python library that provides a range of conformal prediction methods for classification and regression. The library also implements several nonconformity
scores. It has a modular design and can be extended to add new conformal prediction
methods and nonconformities

Tomaž Hočevar

https://www.jstatsoft.org/article/view/v098i07

University of Ljubljana

May 2021

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