Konfidenspoäng

Konfidenspoäng, often translated as confidence score, is a numerical value assigned to a prediction or classification made by a machine learning model. This score indicates how certain the model is about its own output. A high konfidenspoäng suggests that the model is very sure about its prediction, while a low score implies less certainty.

The precise interpretation and calculation of konfidenspoäng vary depending on the specific machine learning algorithm and task. For instance, in classification problems, a konfidenspoäng might represent the probability of an input belonging to a particular class. In object detection, it could signify the likelihood that a bounding box contains a specific object.

Konfidenspoäng are valuable for several reasons. They allow for a more nuanced understanding of model behavior beyond a simple "yes" or "no" answer. Decision-making processes can leverage these scores; for example, a low konfidenspoäng might trigger a human review of a prediction, or allow a system to gracefully fallback to a default action when uncertainty is high. They also play a role in model evaluation and debugging, helping to identify areas where the model struggles or is overconfident. Understanding konfidenspoäng is crucial for deploying machine learning models responsibly and effectively in real-world applications.