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10.1002/minf.201700127 http://scihub22266oqcxt.onion/10.1002/minf.201700127 C5838539!5838539!29360259     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29360259&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Mol+Inform 2018 ; 37 (1-2): ä Nephropedia Template TP {{tp|p=29360259|t=2018. Classifiers and their Metrics Quantified |pdf=|usr=}}{{29360259}} gab.com Text Classifiers and their Metrics Quantified JO: Mol Inform http://www.kidney.de/pget.php?&tw=1&pmid=29360259 #FOAvip Molecular modeling frequently constructs classification models for the prediction of two?class entities, such as compound bio(in)activity, chemical property (non)existence, protein (non)interaction, and so forth. The models are evaluated using well known metrics such as accuracy or true positive rates. However, these frequently used metrics applied to retrospective and/or artificially generated prediction datasets can potentially overestimate true performance in actual prospective experiments. Here, we systematically consider metric value surface generation as a consequence of data balance, and propose the computation of an inverse cumulative distribution function taken over a metric surface. The proposed distribution analysis can aid in the selection of metrics when formulating study design. In addition to theoretical analyses, a practical example in chemogenomic virtual screening highlights the care required in metric selection and interpretation. Twit Text FOAVip Classifiers and their Metrics Quantified ????Mol Inform http://www.kidney.de/pget.php?&tw=1&pmid=29360259 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29360259 #FOAvip English Wikipedia <ref>{{Cite pmid|29360259}}</ref> Classifiers and their Metrics Quantified #MMPMID29360259 Brown JB Mol Inform 2018[Jan]; 37 (1-2): ä PMID29360259show ga Molecular modeling frequently constructs classification models for the prediction of two?class entities, such as compound bio(in)activity, chemical property (non)existence, protein (non)interaction, and so forth. The models are evaluated using well known metrics such as accuracy or true positive rates. However, these frequently used metrics applied to retrospective and/or artificially generated prediction datasets can potentially overestimate true performance in actual prospective experiments. Here, we systematically consider metric value surface generation as a consequence of data balance, and propose the computation of an inverse cumulative distribution function taken over a metric surface. The proposed distribution analysis can aid in the selection of metrics when formulating study design. In addition to theoretical analyses, a practical example in chemogenomic virtual screening highlights the care required in metric selection and interpretation. äClassifiers and their Metrics Quantified (epmc).pdf DeepDyve Pubget Overpricing