parametrisointivirhe
Parametrisointivirhe, known in English as parameterization error, is a term used in statistical modeling and machine learning. It refers to a situation where the chosen model structure does not adequately capture the underlying relationship between the independent and dependent variables in the data. This can happen if the model is too simple to represent the complexity of the data, or if it incorrectly assumes a functional form that is not appropriate.
When a parametrisation error occurs, the model's predictions will be systematically biased, and it may fail
Common causes of parametrisation error include omitting important predictor variables, including irrelevant variables, assuming the wrong