What are the limitations of coverage metrics in AI models?

What are the limitations of coverage metrics in AI models?

Coverage metrics can provide insights into model performance, but they have several limitations. For instance, they may not capture the quality of predictions or the diversity of the data.

Can you explain why coverage metrics might be misleading?

Certainly! Coverage metrics can be misleading because they might give a false sense of security. A model could have high coverage but still perform poorly on unseen data or specific subgroups.

What are some alternative metrics I should consider?

You might consider metrics like precision, recall, F1 score, or AUC-ROC, which provide a more nuanced view of model performance.

How can I improve my model if coverage metrics are not enough?

Improving your model can involve techniques like cross-validation, hyperparameter tuning, and using ensemble methods to enhance performance across different metrics.