What should you select when running early project validation to assess richness?

When running early project validation to assess richness, selecting Recall with Elusion provides a comprehensive view of the model's performance. Recall specifically measures the ability of the model to identify all relevant instances within a dataset. By focusing on Recall, you can ascertain how many of the actual positive cases are correctly being captured by the model, which is essential for determining whether the model is rich enough in its classifications.

Adding the Elusion parameter enhances this assessment by looking at instances that could be misclassifications or overlooked altogether. This combination ensures that not only are you examining the true positives but also actively considering how well the model avoids false negatives, thus providing a deeper insight into its performance.

In early project stages, understanding both recall and potential elusions helps in evaluating if the model has the depth necessary to differentiate between classes effectively, which is critical to ensuring that the final model will perform adequately in real-world scenarios.