Very sparse or small datasets can provide good explanation on interesting details of the plotting subcommands in this extension. Important details are for example the handling of prior knowledge and the computation of the recall prediction in case of random screening.
The following plot shows the result of a collection of 4 records with 3 relevant items (inclusions). The relevant items are found in the following order:
[1, 1, 0, 1, 0]
The black line is an estimate of the recall after every screened record in a naive manner (also refered to as 'random').
The Work Saved over Sampling (WSS) is the difference between the recall of the simulation and the theoretical recall of random screening.
The following graph shows the recall versus the WSS. This comparison is
important because it is the fundamental of the WSS@95% metric used in the
literature about Active Learning for systematic reviewing.
