Filtreerimismõõdusid

Filtreerimismõõdusid refers to the metrics used to evaluate the performance of filtering algorithms. These metrics help quantify how well a filter separates desired data from undesired data. Commonly used measures include precision, recall, and the F1-score. Precision measures the proportion of retrieved items that are relevant, while recall measures the proportion of relevant items that are retrieved. The F1-score is the harmonic mean of precision and recall, providing a balanced measure of a filter's accuracy.

Other important metrics include the area under the receiver operating characteristic (ROC) curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR). The ROC curve plots the true positive rate against the false positive rate at various threshold settings, and AUC-ROC provides a single scalar value summarizing the performance across all thresholds. Similarly, AUC-PR summarizes performance by plotting precision against recall. The choice of which filtreerimismõõdusid to use depends on the specific application and the relative importance of minimizing false positives versus false negatives. For instance, in spam filtering, minimizing false positives (legitimate emails marked as spam) might be prioritized over minimizing false negatives (spam emails reaching the inbox).