Table 2 Evaluation metric
From: Detecting heterogeneity parameters and hybrid models for precision farming
Metrics | Equations | Description |
|---|---|---|
MAPE | \(\frac{100}{n}\sum_{i=1}^{n}\left|\frac{{y}_{i}-{\widehat{y}}_{i}}{{y}_{i}}\right|\) | It is widely used because it is easy to interpret and due to its scale-independency [44]. |
MSE | \(\frac{1}{n}\sum_{i=1}^{n}{\left({y}_{i}-{\widehat{y}}_{i}\right)}^{2}\) | This is good for given weights to outliers that need to be identified [45]. |
R2 | \(1-\frac{\sum {\left({y}_{i}-{\widehat{y}}_{i}\right)}^{2}}{\sum {\left({y}_{i}-\overline{y }\right)}^{2}}\) | This gives the proportion of variance in the dependent variable which can be predicted from the independent variables. \({R}^{2}\) lies between 0 and 1 [45, 46]. |