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      Precrec: fast and accurate precision–recall and ROC curve calculations in R

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      1 , , 1 , 2
      Bioinformatics
      Oxford University Press

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          Abstract

          Summary: The precision–recall plot is more informative than the ROC plot when evaluating classifiers on imbalanced datasets, but fast and accurate curve calculation tools for precision–recall plots are currently not available. We have developed Precrec, an R library that aims to overcome this limitation of the plot. Our tool provides fast and accurate precision–recall calculations together with multiple functionalities that work efficiently under different conditions.

          Availability and Implementation: Precrec is licensed under GPL-3 and freely available from CRAN ( https://cran.r-project.org/package=precrec). It is implemented in R with C ++.

          Contact: takaya.saito@ 123456ii.uib.no

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R

          Summary: Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance. Here, we present the R-package PRROC, which allows for computing and visualizing both PR and ROC curves. In contrast to available R-packages, PRROC allows for computing PR and ROC curves and areas under these curves for soft-labeled data using a continuous interpolation between the points of PR curves. In addition, PRROC provides a generic plot function for generating publication-quality graphics of PR and ROC curves. Availability and implementation: PRROC is available from CRAN and is licensed under GPL 3. Contact: grau@informatik.uni-halle.de
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            Area under Precision-Recall Curves for Weighted and Unweighted Data

            Precision-recall curves are highly informative about the performance of binary classifiers, and the area under these curves is a popular scalar performance measure for comparing different classifiers. However, for many applications class labels are not provided with absolute certainty, but with some degree of confidence, often reflected by weights or soft labels assigned to data points. Computing the area under the precision-recall curve requires interpolating between adjacent supporting points, but previous interpolation schemes are not directly applicable to weighted data. Hence, even in cases where weights were available, they had to be neglected for assessing classifiers using precision-recall curves. Here, we propose an interpolation for precision-recall curves that can also be used for weighted data, and we derive conditions for classification scores yielding the maximum and minimum area under the precision-recall curve. We investigate accordances and differences of the proposed interpolation and previous ones, and we demonstrate that taking into account existing weights of test data is important for the comparison of classifiers.
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              Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23–27, 2013, Proceedings, Part III

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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 January 2017
                01 September 2016
                01 September 2016
                : 33
                : 1
                : 145-147
                Affiliations
                [1 ]Computational Biology Unit, Department of Informatics, University of Bergen, N-5020 Bergen, Norway
                [2 ]Integrated Research Institute (IRI) for the Life Sciences and Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
                Author notes
                [* ]To whom correspondence should be addressed.

                Associate Editor: Jonathan Wren

                Article
                btw570
                10.1093/bioinformatics/btw570
                5408773
                27591081
                23bf26ed-78e6-4326-88f6-a1699285c780
                © The Author 2016. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 April 2016
                : 24 August 2016
                : 26 August 2016
                Page count
                Pages: 3
                Categories
                Applications Notes
                Data and Text Mining

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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