Table 2 Performance of the optimal regression model for each target feature (pressure sensor).
From: Machine learning–based method for analyzing stress distribution in a ship
Target feature (Pressure sensor) | Number of variables (Related pressure sensors) | RMSE | MAE | R2 |
|---|---|---|---|---|
MD1 | 25 | 0.219 | 0.149 | 0.998 |
MD2 | 19 | 0.358 | 0.232 | 0.999 |
MD3 | 14 | 0.315 | 0.223 | 0.998 |
MD4 | 14 | 0.356 | 0.242 | 0.999 |
BM1 | 15 | 0.449 | 0.247 | 0.940 |
BM2 | 25 | 0.257 | 0.166 | 0.984 |
BM3 | 6 | 0.286 | 0.228 | 0.987 |
BM4 | 22 | 0.245 | 0.150 | 0.996 |
BM5 | 23 | 0.439 | 0.310 | 0.999 |
BP1 | 19 | 0.347 | 0.246 | 0.999 |
BP2 | 7 | 0.280 | 0.196 | 0.992 |
BP3 | 26 | 0.659 | 0.399 | 0.996 |
BP4 | 17 | 1.395 | 0.787 | 0.999 |
BP5 | 18 | 0.289 | 0.187 | 0.999 |
BP6 | 8 | 0.264 | 0.205 | 0.999 |
BP7 | 23 | 0.480 | 0.355 | 0.999 |
BP8 | 6 | 0.288 | 0.186 | 0.999 |
BP9 | 20 | 0.320 | 0.216 | 0.999 |
BS1 | 10 | 0.289 | 0.212 | 0.974 |
BS2 | 24 | 0.270 | 0.200 | 0.994 |
BS3 | 8 | 0.251 | 0.176 | 0.983 |
BS4 | 13 | 0.198 | 0.138 | 0.997 |
BS5 | 18 | 0.210 | 0.162 | 0.980 |
BS6 | 7 | 0.219 | 0.180 | 0.999 |
BS7 | 23 | 0.327 | 0.230 | 0.999 |
BS8 | 14 | 0.250 | 0.203 | 0.998 |
BS9 | 22 | 0.287 | 0.218 | 0.999 |
Mean | 0.354 | 0.239 | 0.993 | |
