Fejlkalibrering
Fejlkalibrering, also known as error calibration, is a technique used in various fields, including statistics, machine learning, and decision-making, to adjust predictions or estimates to better reflect their true accuracy. This process is particularly important in situations where initial predictions or estimates may be biased or overconfident. Fejlkalibrering aims to correct these biases by applying a calibration function to the raw predictions, resulting in more reliable and accurate outcomes.
In statistics, fejlkalibrering is often used to adjust probability estimates from models, such as logistic regression
In machine learning, fejlkalibrering is employed to improve the performance of classifiers by adjusting their confidence
In decision-making, fejlkalibrering helps in making more informed choices by providing a better understanding of the
Overall, fejlkalibrering is a valuable tool for enhancing the accuracy and reliability of predictions and estimates,