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010   $a9783030749200$b(electronic bk.)
010   $z9783030749194
024 7 $a10.1007/978-3-030-74920-0
035   $a(MiAaPQ)EBC6845679
035   $a(Au-PeEL)EBL6845679
035   $a(CKB)20462092800041
035   $a(PPN)260833827
035   $a(OCoLC)1293245346
035   $a(DE-He213)978-3-030-74920-0
035   $a(EXLCZ)9920462092800041
100   $a20220106d2021      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 14$aThe 2x2 Matrix $eContingency, Confusion and the Metrics of Binary Classification /$fby A.J. Larner
205   $a1st ed. 2021.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021.
215   $a1 online resource (175 pages)
225 1 $aMathematics and Statistics Series
311 08$aPrint version: Larner, A. J. The 2x2 Matrix Cham : Springer International Publishing AG,c2022 9783030749194 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- Paired measures -- Paired complementary measures -- Unitary measures -- Reciprocal measures -- Other measures, other tables -- Outcome measures not directly related to the 2x2 table -- Index.
330   $aThis book presents and discusses the numerous measures of test performance that can be derived from 2x2 tables. Worked examples based on pragmatic test accuracy study data are used in chapters to illustrate relevance to day-to-day clinical practice. Readers will gain a good understanding of sensitivity and specificity and predictive values along with many other parameters. The contents are highly structured and the use of worked examples facilitates understanding and interpretation. This book is a resource for clinicians in any discipline who are involved in the performance or assessment of test accuracy studies, and professionals in the disciplines of machine learning or informatics wishing to gain insight into clinical applications of 2x2 tables.
410  0$aMathematics and Statistics Series
606   $aBiometry
606   $aNeurology
606   $aMedical informatics
606   $aMachine learning
606   $aBiostatistics
606   $aNeurology
606   $aHealth Informatics
606   $aMachine Learning
615  0$aBiometry.
615  0$aNeurology.
615  0$aMedical informatics.
615  0$aMachine learning.
615 14$aBiostatistics.
615 24$aNeurology.
615 24$aHealth Informatics.
615 24$aMachine Learning.
676   $a519.56
676   $a610.285
700   $aLarner$b A. J.$0782674
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910520073503321
996   $aThe 2x2 Matrix$92584186
997   $aUNINA