Why you need to calculate error bounds on your test metrics

Calculating error bounds on test metrics is crucial for trusting machine learning models' performance estimates, as larger test sets generally offer more reliable results than smaller ones. Confidence or credible intervals provide upper and lower bounds on model performance, helping determine the necessary test set size to confidently meet a target performance level. While calculating credible intervals for simple metrics like accuracy is relatively straightforward, complex metrics such as F1, precision, and recall require more advanced tools like Humanloop's Active Testing. This tool not only computes error bounds but also aids in constructing an effective test set by identifying the most valuable data points to label, potentially reducing annotation efforts by up to 90%. By leveraging credible intervals and advanced testing methodologies, developers can better decide when to trust their models and efficiently allocate resources for data labeling.