Fehlermessgrößen

Fehlermessgrößen, also known as error metrics or error measures, are quantifiable metrics used to assess the accuracy of a predictive model or a measurement. They provide a numerical value that indicates how well a model's predictions or a measurement aligns with the actual or true values. Different situations call for different Fehlermessgrößen, as the importance of certain types of errors can vary.

Common Fehlermessgrößen include Mean Squared Error (MSE), which calculates the average of the squared differences between predicted and actual values, penalizing larger errors more heavily. Root Mean Squared Error (RMSE) is the square root of MSE, providing a measure in the same units as the data. Mean Absolute Error (MAE) calculates the average of the absolute differences, offering a more robust measure against outliers than MSE or RMSE.

For classification tasks, accuracy, precision, recall, and F1-score are frequently used. Accuracy measures the proportion of correct predictions. Precision indicates the proportion of true positive predictions among all positive predictions, while recall (or sensitivity) measures the proportion of true positive predictions among all actual positive instances. The F1-score is the harmonic mean of precision and recall, providing a balanced measure. The choice of Fehlermessgrößen depends on the specific problem and the consequences of different types of errors.