The use case for this visualization is to compare a predictive model
score to an actual outcome (either binary (0/1) or continuous). In this case the
gain curve plot measures how well the model score sorts the data compared
to the true outcome value.

The x-axis represents the fraction of items seen when sorted by score, and the
y-axis represents the cumulative summed true outcome represented by the items seen so far.
See, for example,
https://www.ibm.com/docs/SSLVMB_24.0.0/spss/tutorials/mlp_bankloan_outputtype_02.html.

For comparison, `GainCurvePlot`

also plots the "wizard curve": the gain curve when the
data is sorted according to its true outcome.

To improve presentation quality, the plot is limited to approximately `large_count`

points (default: 1000).
For larger data sets, the data is appropriately randomly sampled down before plotting.