What additional validation metric is calculated if you set a cutoff?

Setting a cutoff in data validation typically relates to classification problems where a decision threshold is established to determine positive versus negative classifications. When a cutoff is applied, it fundamentally influences the distribution of predicted classes, which in turn alters metrics that assess predictive accuracy.

Precision is particularly relevant when a cutoff is in place. It measures the accuracy of positive predictions, giving an understanding of the proportion of true positives among all positive predictions made. Specifically, this means calculating how many of the instances predicted as positive truly belong to the positive class. By setting a cutoff, you can enhance or reduce the strictness of what is considered a positive prediction, thereby directly affecting the precision metric.

Recall, on the other hand, measures the ability of a model to identify all relevant instances. While setting a cutoff can influence recall as well, the direct relationship with the cutoff primarily emphasizes precision. Elusion rate and richness are not standardly recognized metrics in the context related to cutoffs in predictive analysis.

Understanding precision and its connection to classification thresholds helps provide deeper insights into model performance, especially when fine-tuning predictive models for better accuracy in real-world applications.