A labeling function is a computable way to automatically label certain cases.
These labeling function are not of 100% accuracy, but they don't need to be in order to be useful.
Agreements and disagreements between labeling function can be used for active learning, and find efficiently informative functions.
A labeling function that agrees with the concept can be used to find false negatives.
For example, a commit introducing a file is more likely to be adaptive.
Hence, the list of file first commits that are not already labeled as adaptive are likely to provide false negatives and improve recall.
A labeling function disagreeing with the concept can be used to find false positive.
For example, a commit labeled as adaptive that modifies only non source code files, is more likely to be a false positive.
Such examples are helpful in improving the precision.
A labeling function is a computable way to automatically label certain cases.
These labeling function are not of 100% accuracy, but they don't need to be in order to be useful.
Agreements and disagreements between labeling function can be used for active learning, and find efficiently informative functions.
A labeling function that agrees with the concept can be used to find false negatives.
For example, a commit introducing a file is more likely to be adaptive.
Hence, the list of file first commits that are not already labeled as adaptive are likely to provide false negatives and improve recall.
A labeling function disagreeing with the concept can be used to find false positive.
For example, a commit labeled as adaptive that modifies only non source code files, is more likely to be a false positive.
Such examples are helpful in improving the precision.