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

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

• A. It increases precision but lowers recall
• B. It increases recall but lowers precision
• C. It increases both precision and recall
• D. It lowers both precision and recall

Answer: (B)

Choosing a low cutoff in the validation queue increases recall while lowering precision. Recall measures the model's ability to identify all relevant instances, while precision measures the accuracy of those identified instances. By setting a low cutoff, the system is more permissive in classifying instances as relevant, meaning that it captures a larger proportion of true positive cases (thereby increasing recall). However, this leniency also leads to a higher likelihood of false positives, resulting in a decrease in precision. Essentially, more items are classified as relevant at the expense of including some that are not, which is why recall increases and precision decreases with a low cutoff.

Choosing a low cutoff in the validation queue increases recall while lowering precision. Recall measures the model's ability to identify all relevant instances, while precision measures the accuracy of those identified instances. By setting a low cutoff, the system is more permissive in classifying instances as relevant, meaning that it captures a larger proportion of true positive cases (thereby increasing recall). However, this leniency also leads to a higher likelihood of false positives, resulting in a decrease in precision. Essentially, more items are classified as relevant at the expense of including some that are not, which is why recall increases and precision decreases with a low cutoff.