Absoluter Fehler ist ein Begriff, der verwendet wird in statistics and Datenanalyse to quantify the accuracy of a prediction or measurement. It is defined as the absolute difference between the predicted value (or beobachteten Wert) und den tatsächlichen Wert (oder den tatsächlichen Wert). Mathematisch kann er ausgedrückt werden als:
Absolute Error = | Predicted Value – Actual Value |
Diese Metrik ist wichtig, weil sie eine einfache Möglichkeit bietet, zu beurteilen, wie far off a prediction is from the actual result, regardless of the direction of the error (whether the prediction is above or below the actual value). As a result, Absolute Error is always a non-negative number.
Im Kontext von KI und maschinellem Lernen, understanding Absolute Error helps in evaluating the performance of models. For instance, if you are building a regression model to predict housing prices, the Absolute Error will help you understand how close your model’s predictions are to the actual sale prices of the houses. By calculating the Absolute Error across all predictions, you can derive insights about the overall accuracy of the model and identify areas for improvement.
While Absolute Error is a useful metric, it does not provide a normalized view of error sizes, which is why it is often used in conjunction with other metrics, such as Mittlerer absoluter Fehler (MAE) or Root Mean Squared Error (RMSE), which average the Absolute Errors across multiple observations for a more comprehensive assessment of model performance.