The Root Mean Squared Error (RMSE) measures how close the model's predictions are to the actual target values. It is the square root of the Mean Squared Error (MSE), the sum of the squared differences between the predicted value and the actual value. For classification, the 0/1-error is used. $$\operatorname{MSE}(\overline{X})=\operatorname{E}((\overline{X}-\mu)^2)=\left(\frac{\sigma}{\sqrt{n}}\right)^2= \frac{\sigma^2}{n}$$ See: http://en.wikipedia.org/wiki/Mean_squared_error
| Minimum value | 0 |
| Maximum value | 1 |
| Unit | |
| Optimization | Lower is better |