What happens if you choose a high cutoff in the validation queue?

Choosing a high cutoff in the validation queue increases precision but lowers recall due to the nature of how these metrics interact with the threshold used for classification.

When the cutoff is set high, the system becomes more selective about what it classifies as positive results. This means that only those entries that are very likely to be relevant will be flagged. As a result, when something is classified as positive, it has a higher probability of being a true positive, thus improving precision. Precision is defined as the ratio of true positives to the sum of true positives and false positives. So, with fewer false positives at a high cutoff, precision increases.

On the other hand, recall measures the ability to identify all relevant items, defined as the ratio of true positives to the sum of true positives and false negatives. By setting a high cutoff, the system may miss many true positive cases, as only the most confidently relevant entries are flagged, leading to an increase in false negatives. Thus, recall decreases because fewer actual positive cases are identified when the cutoff is too stringent.

This dynamic is a common aspect of classification problems, where tuning the cutoff can lead to a trade-off between precision and recall. Therefore, choosing a high cutoff results in an increased precision alongside a decreased recall