Modellgranskning

Modellgranskning, often translated as model review or model scrutiny, is a systematic process of evaluating and validating statistical or machine learning models. The primary goal of modellgranskning is to ensure that a model is fit for its intended purpose, reliable, and performs as expected. This involves a thorough examination of the model's assumptions, data inputs, development process, and performance metrics.

During modellgranskning, experts assess various aspects of the model. This can include verifying the appropriateness of the chosen modeling techniques, checking for potential biases in the data used for training and testing, and ensuring that the model's parameters have been estimated correctly. The review also scrutinizes the model's performance, comparing its predictions against actual outcomes and evaluating metrics such as accuracy, precision, recall, and AUC.

The importance of modellgranskning is amplified in critical applications such as finance, healthcare, and autonomous systems, where model errors can have significant consequences. It helps to identify potential flaws, such as overfitting or underfitting, which can lead to poor generalization to new data. Furthermore, modellgranskning contributes to regulatory compliance and builds trust in the models used for decision-making. The process can be conducted internally by development teams or externally by independent auditors or domain experts, depending on the context and requirements.