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 to generalize well to new, unseen data. For example, if a linear regression model is used to describe a clearly non-linear relationship, a parametrisation error exists. This can lead to inaccurate conclusions drawn from the analysis.

Common causes of parametrisation error include omitting important predictor variables, including irrelevant variables, assuming the wrong functional form (e.g., linear instead of polynomial or exponential), or incorrectly specifying interactions between variables. Detecting and addressing parametrisation errors is a crucial part of the model building process. This often involves diagnostic checks, residual analysis, and comparing different model specifications. The goal is to select a model that is both a good fit for the observed data and capable of making reliable predictions.