TY - GEN
T1 - Null QQ plots
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
AU - Berrar, Daniel
PY - 2012/12/1
Y1 - 2012/12/1
N2 - The evaluation of machine learning algorithms is commonly based on statistical significance tests. However, the suitability of such tests is often questionable. We propose null QQ plots as a simple yet powerful graphical alternative to significance testing. Using ten benchmark data sets, we demonstrate that these plots concisely summarize the essential results from a comparative classification study, while they are easy to produce and interpret.
AB - The evaluation of machine learning algorithms is commonly based on statistical significance tests. However, the suitability of such tests is often questionable. We propose null QQ plots as a simple yet powerful graphical alternative to significance testing. Using ten benchmark data sets, we demonstrate that these plots concisely summarize the essential results from a comparative classification study, while they are easy to produce and interpret.
UR - http://www.scopus.com/inward/record.url?scp=84874581269&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874581269&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84874581269
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1852
EP - 1855
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
Y2 - 11 November 2012 through 15 November 2012
ER -