Mittlerer quadratischer Fehler (MSE) is a statistical metric used to evaluate the accuracy of a model’s predictions by quantifying the difference between predicted values and the actual values observed in the data.
Die Formel zur Berechnung des MSE lautet:
MSE = (1/n) * Σ(actual – predicted)²
Hier ist n is the number of observations, actual represents the true values, and predicted are the values generated by the model. The squared differences are used to ensure that positive and negative errors do not cancel each other out, emphasizing larger errors more than smaller ones.
MSE wird häufig in Regressionsanalyse and maschinellem Lernen to assess how well a model performs. A lower MSE value indicates better Modellleistung, as it signifies that the predictions are closer to the actual values. Conversely, a higher MSE indicates larger errors and poorer model accuracy.
While MSE is a useful metric, it is important to note that it is sensitive to outliers due to the squaring of errors. Therefore, in cases where the data may contain outliers, other metrics like Mittlerer absoluter Fehler (MAE) könnte ebenfalls für die Bewertung in Betracht gezogen werden.
Zusammenfassend ist der mittlere quadratische Fehler ein grundlegendes Konzept in prädiktiven Modellierungen, providing a clear numeric value that reflects the quality of a model’s predictions.