Korrekthetsmått

Korrekthetsmått, a Swedish term often translated as "correctness measure" or "accuracy metric," refers to a quantitative evaluation used to assess the performance of a system or model. These measures are fundamental in fields like machine learning, natural language processing, and computer vision, where the goal is to determine how well a system's outputs align with expected or ground truth data. The specific type of korrekthetsmått employed depends heavily on the nature of the task and the data involved.

Common korrekthetsmått include accuracy, which calculates the proportion of correct predictions out of the total number of predictions. Precision measures the proportion of true positives among all positive predictions, highlighting the system's ability to avoid false positives. Recall, on the other hand, assesses the proportion of true positives among all actual positive instances, focusing on the system's ability to find all relevant items. F1-score is a harmonic mean of precision and recall, providing a balanced metric. For tasks involving ranking or prediction of continuous values, other measures like Mean Squared Error (MSE) or Mean Absolute Error (MAE) are utilized. The careful selection and interpretation of appropriate korrekthetsmått are crucial for understanding a system's strengths and weaknesses, guiding further development and optimization.