|Authors:||Tomaž Hočevar, Blaž Zupan, Jonna Stålring|
|Title:||Conformal Prediction with Orange|
|Abstract:||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.|
Page views:: 1299. Submitted: 2018-06-28. Published: 2021-05-31.
Conformal Prediction with Orange
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