Modellvorhersagefehler
Modellvorhersagefehler, also known as model prediction error, refers to the discrepancy between the predictions made by a statistical or computational model and the actual observed values. These errors are an inherent part of any modeling process, as models are simplifications of reality and rarely capture all underlying complexities. Understanding and quantifying these errors is crucial for evaluating the performance and reliability of a model.
Errors can arise from various sources. Model misspecification occurs when the chosen model structure does not
The magnitude and nature of model prediction errors are often assessed using error metrics. Common metrics
Minimizing model prediction errors is a primary goal in model development. This can be achieved through careful