Prediksjonsfeilen

Prediksjonsfeilen, also known as prediction error or forecasting error, is a measure of the difference between the predicted values and the actual values in a dataset. It is a critical concept in fields such as statistics, machine learning, and economics, where accurate predictions are often sought. The prediction error can be quantified using various metrics, with the most common being the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These metrics provide a numerical value that represents the average magnitude of the errors or the average distance between the predicted and actual values. A lower prediction error indicates a more accurate prediction model. Reducing prediction error is a primary goal in model development, as it directly impacts the reliability and usefulness of the predictions. Techniques to minimize prediction error include feature engineering, model selection, hyperparameter tuning, and regularization. Understanding and managing prediction error is essential for making informed decisions based on predictive models.