Virhemittarit
Virhemittarit, often translated as error metrics or error measures, are quantitative indicators used in various fields, particularly in machine learning and statistics, to evaluate the performance of predictive models. They provide a way to assess how well a model's predictions align with the actual observed values. The choice of virhemittarit depends heavily on the specific problem and the nature of the data.
Common examples of virhemittarit include Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) for regression
The interpretation of virhemittarit is crucial. A lower value generally indicates a better-performing model for error-based