Which characteristic is involved in a learning curve?

A learning curve visually represents how a model's performance, typically measured in terms of accuracy, error rate, or another metric, changes with the number of training examples. This graph illustrates the relationship between the size of the training dataset and the model's performance, highlighting how more data can lead to improved learning and a decrease in error. As the model is exposed to additional training data, it can learn patterns more effectively, which is reflected in the curve's trajectory.

The other characteristics, while they relate to various aspects of model evaluation and development, do not capture the essence of a learning curve. For example, comparison of model execution time is focused on efficiency rather than performance over training data size. Tracking changes in model complexity addresses how adjustments to model architecture affect performance but does not inherently involve demonstrating this change through a learning curve. Similarly, assessment of model scalability is related to how well a model performs as it is applied to larger datasets or more complex problems, but it does not specifically correspond to visualizing performance changes due to training data. Therefore, the visualization of change in score or error is fundamentally what defines a learning curve.