Final answer:
A Cost Curve is a suitable visualization for model performance when class priors and cost-benefit estimates are known and stable, as it integrates cost-benefit trade-offs of a binary classifier. ROC curves are less helpful in this scenario since they do not incorporate these factors. Ensure visualizations are clear and accessible, appropriately scaled, and suitable for the intended audience.
Step-by-step explanation:
When both class priors (proportion of positive & negative instances) and cost-benefit estimates are known and expected to be stable, a good choice for visualizing model performance could be the Cost Curve. The cost curve is a tool used in the field of machine learning and data science to visualize the cost-benefit trade-offs of different probability thresholds for a binary classifier. It plots the expected cost on one axis against the proportion of positive instances that are predicted as positive on the other.
Another informative visualization tool is the Receiver Operating Characteristic (ROC) curve, which plots the true positive rate against the false positive rate. However, the ROC curve does not take into account class priors or cost-benefit considerations directly. When these factors are critical to the decision-making process, the cost curve or a profit curve, which considers the financial impact of true and false positives and negatives, would be more suitable.
It is also possible to utilize a confusion matrix complemented with cost information, which directly presents the numbers of true positives, false negatives, true negatives, and false positives, along with their respective costs. However, a visualization that incorporates the probability threshold like cost curves provides a more comprehensive overview of model performance across different decision thresholds.
When designing visualizations, it's important to ensure clarity and accessibility. Labels should be legible, and axes properly scaled to reflect the data at hand, ensuring that smaller or more discrete segments are easily visible. The visualization should also be appropriate for the audience, providing the level of detail necessary for making informed decisions based on the model's performance.