What is represented on a lift chart?

A lift chart visually represents the effectiveness of a predictive model by displaying the improvement achieved over a baseline. Specifically, it illustrates the ratio of the percentage of actual positive outcomes (or examples) identified by the model compared to what would be expected without using the model, often referred to as a random selection.

In this context, the lift chart enables users to assess how well the model is performing relative to a baseline of random guessing. By plotting the cumulative gains of using the model against the cumulative gains of the baseline, one can visually analyze the model's performance across different segments of the data. A higher lift indicates better performance, as the model successfully identifies a greater portion of positive outcomes than would be found by chance.

The other options do not accurately capture the purpose of the lift chart. For example, the first choice relates to prediction accuracy but does not align with the specific function of the lift chart. The total count of data points in training does not pertain to the chart's purpose, and the complexity of the model is not represented directly within a lift chart, which focuses on performance metrics rather than model specifications.