Model accuracy is typically assessed using several metrics: - Error Metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) measure the differences between predicted and observed values. - Correlation Coefficients: The R-squared (R²) value indicates how well the predicted values correlate with actual data. - Validation Techniques: Cross-validation and external validation with independent datasets are commonly used to test model robustness.
