Another method of assessing a statistical model's performance is by evaluating the model's growth of learning or the model's ability to improve learning (obtain a better score) with additional experience (for example, more rounds of cross-validation).
The phrase, with additional experience, is vitally important in statistics as we not only look for a statistical model to perform well on a given population of data, but we hope that the model's performance will improve as it is trained and tested on more and more data.
The information indicating a model's performance, result, or score with a data file population is usually combined with other scores to show a line or curve—this is known as the statistical model's learning curve. ...
