{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T19:34:30Z","timestamp":1767209670891,"version":"build-2238731810"},"posted":{"date-parts":[[2018,3,16]]},"group-title":"PeerJ Preprints","reference-count":0,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2018,3,16]],"date-time":"2018-03-16T00:00:00Z","timestamp":1521158400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Nowadays, there is a large number of machine learning models that could be used for various areas. However, different research targets are usually sensitive to the type of models. For a specific prediction target, the predictive accuracy of a machine learning model is always dependent to the data feature, data size and the intrinsic relationship between inputs and outputs. Therefore, for a specific data group and a fixed prediction mission, how to rationally compare the predictive accuracy of different machine learning model is a big question. In this brief note, we show how should we compare the performances of different machine models by raising some typical examples.<\/jats:p>","DOI":"10.7287\/peerj.preprints.26714v1","type":"posted-content","created":{"date-parts":[[2018,3,16]],"date-time":"2018-03-16T10:14:22Z","timestamp":1521195262000},"source":"Crossref","is-referenced-by-count":6,"title":["Technical note: how to rationally compare the performances of different machine learning models?"],"prefix":"10.7287","author":[{"given":"Terazima","family":"Maeda","sequence":"first","affiliation":[{"name":"Engineering, Nagoya University, Nagoya, Japan"}]}],"member":"4443","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/peerj.com\/preprints\/26714v1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/26714v1.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/26714v1.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/26714v1.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T18:50:01Z","timestamp":1577127001000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/preprints\/26714v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,16]]},"references-count":0,"aliases":["10.7287\/peerj.preprints.26714"],"URL":"https:\/\/doi.org\/10.7287\/peerj.preprints.26714v1","relation":{},"subject":[],"published":{"date-parts":[[2018,3,16]]},"subtype":"preprint"}}