Measure

# root_mean_squared_error

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

Source Code:
See WEKA's Evaluation class


## Properties

 Minimum value 0 Maximum value 1 Unit Optimization Lower is better