Modelvurdering

Modelvurdering, or model evaluation, is the process of assessing the performance and suitability of a statistical or machine learning model for a given task. It aims to estimate how well the model will perform on unseen data and under real-world conditions, and to inform decisions about deployment, maintenance, and governance. The term is used in data science contexts to describe the formal examination of a model’s predictive capabilities and limitations.

Key aspects of modelvurdering include selection of appropriate evaluation metrics, assessment of calibration, robustness, fairness, and interpretability. Depending on the task, common metrics include accuracy, precision, recall, F1 score, and AUC-ROC for classification; mean absolute error, root mean squared error, and R-squared for regression; and rank-based metrics for ordering tasks. Calibration analyzes how well predicted probabilities reflect observed frequencies. Robustness checks test performance under perturbations, noise, or distribution shifts. Fairness assessments examine potential biases across demographic or other groups, while interpretability analyses explore the contribution of features to predictions.

The typical workflow involves preparing data, splitting into training, validation, and test sets, and using cross-validation or other resampling methods to estimate performance while reducing overfitting. Documentation of methodology, data provenance, and model assumptions is essential for reproducibility. Post-deployment, ongoing monitoring for drift, calibration decay, and changes in data distributions is recommended, with planned retraining and re-evaluation as needed.

Limitations of modelvurdering include reliance on historical data that may not capture future conditions, potential overemphasis on specific metrics, and the need to balance performance with ethical, legal, and societal considerations. Effective modelvurdering supports responsible deployment and continuous improvement of predictive systems.