How is the accuracy of an AI model evaluated?

The quality of an AI model is assessed using clearly defined performance metrics. They show transparently how reliably a machine learning model performs its task: measurable, comparable, and verifiable over time.

Which metrics are appropriate depends on the specific use case. For forecasting models, the key question is how close the prediction is to the actual outcome. Typical metrics include:

• R² (coefficient of determination): Shows how well the model explains the variance in the target values.
• MAE (Mean Absolute Error): Measures the average absolute deviation between prediction and reality.
• RMSE (Root Mean Squared Error): Weights larger errors more heavily and shows average deviation in the unit of the target variable.
• MAPE (Mean Absolute Percentage Error): Expresses the average deviation as a percentage.

For classification models, the evaluation focuses on how often the model makes correct decisions and which types of errors occur. Common metrics include Precision (accuracy of positive predictions), Recall (hit rate), and the F1-Score (harmonic mean of Precision and Recall).

Beyond that, generalization ability is critical: does the model perform well only on training data or also on new, unseen data? It is also assessed whether data structures change over time, causing the model to lose predictive power through data drift or prediction drift.

In addition to statistical quality, economic impact matters. A model is successful when it measurably improves processes, saves time, or reduces errors. Only the combination of technical accuracy, stability, and economic value shows whether an AI model delivers in production.