document: Methods for outlier detection in prediction If a prediction sample is different from the calibration samples , it can be considered as an outlier in prediction . In this work , two techniques , the use of uncertainty estimation and the convex hull method are studied to detect such prediction outliers . Classical techniques -LRB- Mahalanobis distance and X-residuals -RRB- , potential functions and robust techniques are used for comparison . It is concluded that the combination of the convex hull method and uncertainty estimation offers a practical way for detecting outliers in prediction . By adding the potential function method , inliers can also be detected
doc_bio_tags: O O B I O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O B I I O O O O O O O O O O O B I O O O O B I O B I O O O O O O O O O O O O O B I I O B I O O O O O O O O O O O O O O O O O B O O O O
extractive_keyphrases: outlier detection prediction sample calibration samples uncertainty estimation convex hull method mahalanobis distance potential functions robust techniques inliers
abstractive_keyphrases: x-residuals