{"content":[{"text":"Journal of Statistical Software \n May 2021, Volume 98, Issue 7. doi: 10.18637/jss.v098.i07","bbox":{"left":0.315,"top":0.10677,"width":0.5619480000000001,"height":0.059923000000000004}},{"text":"JSS","bbox":{"left":0.136071,"top":0.12772,"width":0.02979799999999999,"height":0.01836099999999999}},{"text":"Conformal Prediction with Orange","bbox":{"left":0.263883,"top":0.254786,"width":0.48523400000000005,"height":0.029283000000000003}},{"text":"Tomaž Hočevar \n University of Ljubljana","bbox":{"left":0.172533,"top":0.312555,"width":0.18579,"height":0.035997}},{"text":"Blaž Zupan \n University of Ljubljana","bbox":{"left":0.416689,"top":0.312555,"width":0.18579,"height":0.035997}},{"text":"Jonna Stålring \n Medivir AB","bbox":{"left":0.667625,"top":0.312555,"width":0.14460099999999998,"height":0.035997}},{"text":"Abstract \n Conformal predictors estimate the reliability of outcomes made by supervised machine \n learning models. Instead of a point value, conformal prediction deﬁnes an outcome region \n that meets a user-speciﬁed reliability threshold. Provided that the data are indepen- \n dently and identically distributed, the user can control the level of the prediction errors \n and adjust it following the requirements of a given application. The quality of conformal \n predictions often depends on the choice of nonconformity estimate for a given machine \n learning method. To promote the selection of a successful approach, we have developed \n Orange3-Conformal, a Python library that provides a range of conformal prediction meth- \n ods for classiﬁcation and regression. The library also implements several nonconformity \n scores. It has a modular design and can be extended to add new conformal prediction \n methods and nonconformities.","bbox":{"left":0.181887,"top":0.388719,"width":0.6495449999999999,"height":0.18078900000000003}},{"text":"Keywords: conformal prediction, nonconformity, machine learning, Python, Orange.","bbox":{"left":0.136071,"top":0.581767,"width":0.6663209999999999,"height":0.018957}},{"text":"1. Introduction \n The application of supervised machine learning tools is growing, and an increasing number \n of decisions in a wide range of industries are based on computer-generated predictions. The \n eﬀects of prediction errors vary between diﬀerent applications. For example, forecasts of \n recommender systems that aim to increase sales in e-commerce are less critical than those \n of diagnostic systems in the intensive care unit. In applications where the reliability of \n individual predictions is crucial, quantiﬁcation of the prediction conﬁdence should complement \n the output of a machine learning method. \n A range of current prediction conﬁdence or reliability methods employs sensitivity analysis, \n inference of reliability from predictions of near neighbors, or explore properties of individual \n machine learning algorithms, such as tree variance in a random forest (Bosnić and Kononenko \n 2009). Conformal predictions (Vovk et al. 2005) are conceptually diﬀerent from other reliabil- \n ity methods in predicting a range of values rather than reporting a conﬁdence value. Provided","bbox":{"left":0.136071,"top":0.653366,"width":0.741274,"height":0.23302899999999993}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}