From: Predicting rice blast disease: machine learning versus process-based models
Training data* | Test data | Train | Test | ||||||
---|---|---|---|---|---|---|---|---|---|
r (a) | r (b) | r (a) | r (b) | MAE** (a) | MAE** (b) | r2 (a) | r2 (b) | ||
k2015 + k2016 | s2016 | 0.94 | 0.94 | 0.56 | 0.72 | 0.78 | 0.63 | 0.31 | 0.52 |
k2015 + k2016 | p2015 | 0.21 | 0.31 | 0.60 | 0.88 | 0.04 | 0.10 | ||
k2015 + p2015 | k2016 | 0.87 | 0.85 | 0.69 | 0.68 | 0.78 | 0.53 | 0.48 | 0.46 |
k2015 + p2015 | s2016 | 0.67 | 0.57 | 0.91 | 0.71 | 0.45 | 0.32 | ||
k2015 + s2016 | k2016 | 0.92 | 0.95 | 0.46 | 0.71 | 0.39 | 0.51 | 0.21 | 0.66 |
k2015 + s2016 | p2015 | 0.22 | 0.25 | 1.04 | 1.10 | 0.05 | 0.06 | ||
k2016 + p2015 | k2015 | 0.92 | 0.94 | 0.61 | 0.69 | 0.16 | 0.48 | 0.37 | 0.47 |
k2016 + p2015 | s2016 | 0.52 | 0.58 | 0.98 | 0.69 | 0.27 | 0.33 | ||
k2016 + s2016 | k2015 | 0.89 | 0.92 | 0.36 | 0.73 | 0.40 | 0.52 | 0.13 | 0.62 |
k2016 + s2016 | p2015 | 0.17 | 0.35 | 1.20 | 0.78 | 0.03 | 0.12 | ||
s2016 + p2015 | k2015 | 0.91 | 0.94 | 0.33 | 0.63 | 0.80 | 0.63 | 0.11 | 0.40 |
s2016 + p2015 | k2016 | 0.19 | 0.57 | 0.72 | 0.62 | 0.04 | 0.32 | ||
Average | 0.42 | 0.57 | 0.73 | 0.67 | 0.21 | 0.37 |