Table 3.

Overview of performance metrics for regression analysis.

Error	Mathematical formulation	Description
Mean squared logarithmic error (MSLE)		Measures the average squared difference between the logarithm of predicted values and the logarithm of actual values, reducing sensitivity to large errors
Root mean squared logarithmic error (RMSLE)		Represents the root of the mean squared logarithmic error, translating logarithmic-scale errors back to interpretable units
Variance score (R-squared)		Indicates the proportion of variance in the target variable explained by the model, ranging from 0 (no fit) to 1 (perfect fit)
Mean absolute error (MAE)		Calculates the mean absolute deviation between predictions and true values, treating all errors equally regardless of magnitude
Mean absolute percentage error (MAPE)		Expresses errors as a percentage of actual values, enabling intuitive comparisons across datasets with differing scales
Root mean squared error (RMSE)		Reflects the standard deviation of prediction errors, emphasizing severe inaccuracies over minor ones
Mean squared error (MSE)		Computes the mean of squared deviations, disproportionately penalizing large errors compared to small ones