# Cumulative Gains and Lift Charts

The cumulative gains chart shows the percentage of the
overall number of cases in a given category "gained" by targeting
a percentage of the total number of cases. For example, the first
point on the curve for the *Total service* category is approximately at (10%, 20%), meaning that if you score
a dataset with the network and sort all of the cases by predicted
pseudo-probability of *Total service*, you would expect the top 10% to contain approximately 20% of all
of the cases that actually take the category *Total service*. Likewise, the top 20% would contain approximately
30% of the defaulters, the top 30% of cases, 50% of defaulters, and
so on. If you select 100% of the scored dataset, you obtain all of
the defaulters in the dataset.

The diagonal line is the "baseline" curve; if you select 10% of the cases from the scored dataset at random, you would expect to "gain" approximately 10% of all of the cases that actually take any given category. The farther above the baseline a curve lies, the greater the gain.

The lift chart is derived from the cumulative gains chart;
the values on the *y* axis correspond
to the ratio of the cumulative gain for each curve to the baseline.
Thus, the lift at 10% for the category *Total
service* is approximately 20%/10% = 2.0. It provides another
way of looking at the information in the cumulative gains chart.

*Note*:
The cumulative gains and lift charts are based on the combined training
and testing samples.