Average at Your Own Risk: The Metric Setting That Can Reverse the Winner
TL;DR for operators The classifiers have been tested, the predictions are fixed, and the evaluation meeting expects the metric table to reveal an obvious winner. Yet on the yeast multilabel dataset, one way of averaging F1 ranked BR-kNN first and BR-SVM last, while another produced the exact opposite ordering. Nothing about the models or predictions changed; only the unit given equal influence changed. ...