tundlikkusmõõdikud

Tundlikkusmõõdikud, also known as sensitivity metrics or accuracy metrics, are statistical measures used to evaluate the performance of a classification model, particularly in distinguishing between positive and negative cases. They are crucial in scenarios where correctly identifying positive instances is of paramount importance.

The primary tundlikkusmõõdik is "tundlikkus" itself, also commonly referred to as recall or true positive rate. It quantifies the proportion of actual positive cases that are correctly identified by the model. Mathematically, it is calculated as the number of true positives divided by the sum of true positives and false negatives. A high sensitivity score indicates that the model is good at finding all the positive instances.

Another important metric often discussed alongside sensitivity is "spetsiifilisus" or specificity, also known as the true negative rate. Specificity measures the proportion of actual negative cases that are correctly identified by the model. It is calculated as the number of true negatives divided by the sum of true negatives and false positives.

While sensitivity focuses on identifying all positive cases, specificity focuses on correctly identifying all negative cases. The choice between prioritizing sensitivity or specificity depends heavily on the specific application and the costs associated with false positives and false negatives. For example, in medical diagnosis for a life-threatening disease, high sensitivity is often preferred to ensure no positive cases are missed, even if it leads to a higher number of false positives requiring further testing. Conversely, in spam detection, high specificity might be more desirable to avoid marking legitimate emails as spam. Other related metrics, such as precision and the F1-score, are also commonly used to provide a more comprehensive evaluation of a model's classification performance by considering both sensitivity and specificity.