What do Response Rate Charts plot in relation to model selection?

Response Rate Charts are used to visualize the effectiveness of different models by plotting the proportion of selected prediction cases. This visual representation helps in evaluating how well a model performs across various thresholds or cut-off points. It provides insight into the model's ability to correctly classify or predict positive outcomes versus negative ones, thereby allowing for a direct comparison of various models based on how many of the predicted cases are actually relevant or correct.

When selecting a model, practitioners can effectively use the Response Rate Chart to determine which model yields the highest proportion of accurate predictions. This is particularly useful when dealing with imbalanced datasets where some classes may be underrepresented. By utilizing this chart, one can make informed decisions that align with the goals of the analysis, such as maximizing accuracy or optimizing for certain metrics.

In contrast, other options like the time taken for model training, effects of data size on predictions, or the statistical significance of input variables, do not directly relate to the mechanism of evaluating model performance through prediction rates. They instead focus on different aspects of the modeling process, leaving Response Rate Charts as a pivotal tool for assessing how well models are doing with respect to their predictive capabilities.