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
lift 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 lift seen so far (cumulative value of model over cummulative value of random selection)..
For comparison, LiftCurvePlot
also plots the "wizard curve": the lift 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.