Table 8 Used prediction accuracy measures
From: Prediction accuracy measurements as a fitness function for software effort estimation
ID | Name | Equations |
|---|---|---|
1 | Least absolute deviations (LAD) | \(LAD=\sum \limits _{i=1}^{n}\left| y_i-\hat{y}_i\right|\) |
2 | Mean absolute error (MAE) | \(MAE=\frac{1}{n}\sum \limits _{i=1}^{n}\left| y_i-\hat{y}_i\right|\) |
3 | Mean squared error (MSE) | \(MSE=\frac{1}{n}\sum \limits _{i=1}^{n}\left( y_i-\hat{y}_i\right) ^2\) |
4 | Root mean squared error (RMSE) | \(RMSE=\sqrt{\frac{1}{n}\sum \limits _{i=1}^{n}\left( y_i-\hat{y}_i\right) ^2}\) |
5 | Mean magnitude of relative error (MMRE) | \(MMRE=\frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{y_i}\) |
6 | Median magnitude of relative error (MdMRE) | \(MdMRE=median\left( \frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{y_i}\right)\) |
7 | MMRE relative to the estimate (MEMRE) | \(MEMRE\,=\,\frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{\hat{y}_i}\) |
8 | MdMRE relative to the estimate (MdEMRE) | \(MdEMRE\,=\,median\left( \frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{\hat{y}_i}\right)\) |
9 | R squared (\(R^2\)) | \(R^2=1-\frac{\sum \limits _{i\,=\,1}^{n}\left( y_i-\hat{y}_i\right) ^2}{\sum \limits _{i=1}^{n}\left( y_i-\bar{y}\right) ^2}\) |
10 | Prediction within 25 % Pred(25) | \(Pred(25)=\frac{Number\;of\;projects,\;where\;(MRE\le 0.25)}{Number\;of\;projects}\) |