The following metrics apply to regression tasks:
| Metric | Description | Use case |
|---|---|---|
| MAE | Mean Absolute Error. The average absolute difference between predicted and actual values. Direction of error does not matter. | When you want to minimize average prediction error. |
| MSE | Mean Squared Error. The average of squared differences between predicted and actual values. Always non-negative, lower is better. Penalizes large errors more strongly. | When large individual errors should be penalized. |
| R² | Coefficient of Determination. Measures the proportion of variance in the target variable explained by the model. A value of 1 means perfect predictions, 0 means no explanatory power. | When you want to know how well the model explains the variance in the data. |
| RMSE | Root Mean Squared Error. The square root of MSE. Lower is better. | When large individual errors should be penalized heavily. |
| RMSLE | Root Mean Squared Logarithmic Error. Lower is better. Penalizes underestimates more than overestimates. | When large differences between high-value predictions should be penalized less. |
| RMSPE | Root Mean Squared Percentage Error. Measures percentage error between predicted and actual values. Lower is better. Rows with 0 in the target variable are excluded. | When you need errors expressed as percentages. |
| Max AE | Maximum Absolute Error. The largest absolute difference between predicted and actual values. | When you want to understand the maximum possible deviation. |
| Min AE | Minimum Absolute Error. The smallest absolute difference between predicted and actual values. It indicates the best-case prediction accuracy for individual samples. | When you want to see how close the model can get to perfect predictions. |