A Bayesian approach for comparing cross-validated algorithms on multiple data sets
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| 001 | 605478244 | ||
| 003 | CHVBK | ||
| 005 | 20210128100404.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5486-z |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5486-z | ||
| 245 | 0 | 2 | |a A Bayesian approach for comparing cross-validated algorithms on multiple data sets |h [Elektronische Daten] |c [Giorgio Corani, Alessio Benavoli] |
| 520 | 3 | |a We present a Bayesian approach for making statistical inference about the accuracy (or any other score) of two competing algorithms which have been assessed via cross-validation on multiple data sets. The approach is constituted by two pieces. The first is a novel correlated Bayesian $$t$$ t test for the analysis of the cross-validation results on a single data set which accounts for the correlation due to the overlapping training sets. The second piece merges the posterior probabilities computed by the Bayesian correlated $$t$$ t test on the different data sets to make inference on multiple data sets. It does so by adopting a Poisson-binomial model. The inferences on multiple data sets account for the different uncertainty of the cross-validation results on the different data sets. It is the first test able to achieve this goal. It is generally more powerful than the signed-rank test if ten runs of cross-validation are performed, as it is anyway generally recommended. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Bayesian hypothesis tests |2 nationallicence | |
| 690 | 7 | |a Signed-rank test |2 nationallicence | |
| 690 | 7 | |a Cross-validation |2 nationallicence | |
| 690 | 7 | |a Poisson-binomial |2 nationallicence | |
| 690 | 7 | |a Hypothesis test |2 nationallicence | |
| 690 | 7 | |a Evaluation of classifiers |2 nationallicence | |
| 700 | 1 | |a Corani |D Giorgio |u Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), Manno, Switzerland |4 aut | |
| 700 | 1 | |a Benavoli |D Alessio |u Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), Manno, Switzerland |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 285-304 |x 0885-6125 |q 100:2-3<285 |1 2015 |2 100 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5486-z |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-015-5486-z |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Corani |D Giorgio |u Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), Manno, Switzerland |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Benavoli |D Alessio |u Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), Manno, Switzerland |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 285-304 |x 0885-6125 |q 100:2-3<285 |1 2015 |2 100 |o 10994 | ||