The evaluation metric for this competition is macro
[F1-Score](https://en.wikipedia.org/wiki/F-score)
. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:
F1 = 2 p r / [p + r] where p = tp / [tp + fp] and r = tp / [tp +fn]
The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.
The macro F1 is the arithmetic mean of the F1-score of each class.
Submission Format
For every author in the dataset
, submission files should contain two columns: ID and Code. Code should be a comma-delimited list.
The file should contain a header of ID and Code and have the following format:
ID=imagefilename.csv, Code={1, 2, 3, 4, 5, 6}