Table 8 Performance of the proposed AI models applied to Dataset 1 using the 10-fold cross-validation technique.
From: Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective
Modal name | Train set | Test set | ||||
|---|---|---|---|---|---|---|
MAE (%) ± STD | MSE (%) ± STD | R2 score (%) ± STD | MAE (%) ± STD | MSE (%) ± STD | R2 score (%) ± STD | |
GB | 0.00006 ± 0.00008 | 0.00000 ± 0.00000 | 0.99999 ± 0.00001 | 0.00662 ± 0.00530 | 0.00097 ± 0.00139 | 0.90809 ± 0.12447 |
RF | 0.00056 ± 0.00026 | 0.00003 ± 0.00004 | 0.99549 ± 0.00500 | 0.00755 ± 0.00573 | 0.00116 ± 0.00152 | 0.89595 ± 0.1414 |
HGB | 0.00667 ± 0.00329 | 0.00062 ± 0.00052 | 0.83340 ± 0.12453 | 0.02315 ± 0.01142 | 0.00482 ± 0.00391 | 0.65269 ± 0.1746 |
ETR | 0.00021 ± 0.00010 | 0.00002 ± 0.00004 | 0.98994 ± 0.00474 | 0.00585 ± 0.00541 | 0.00083 ± 0.00137 | 0.92041 ± 0.13056 |
BR | 0.00069 ± 0.00030 | 0.00045 ± 0.00016 | 0.99543 ± 0.00389 | 0.00800 ± 0.00596 | 0.00119 ± 0.00152 | 0.89882 ± 0.13756 |
ABR | 0.00372 ± 0.00278 | 0.00082 ± 0.00056 | 0.99534 ± 0.00249 | 0.01030 ± 0.00630 | 0.00121 ± 0.00150 | 0.89823 ± 0.12741 |
DT | 0.00022 ± 0.00011 | 0.00001 ± 0.00002 | 0.99811 ± 0.00362 | 0.00731 ± 0.00528 | 0.00114 ± 0.00144 | 0.90164 ± 0.12139 |
Stacking1 | 0.00128 ± 0.00064 | 0.00009 ± 0.00011 | 0.96709 ± 0.04535 | 0.00980 ± 0.00606 | 0.00165 ± 0.00169 | 0.85701 ± 0.15285 |
Stacking2 | 0.00028 ± 0.00015 | 0.00001 ± 0.00003 | 0.99722 ± 0.00374 | 0.00583 ± 0.00482 | 0.00082 ± 0.00141 | 0.91730 ± 0.13410 |
VotingR1 | 0.00429 ± 0.00181 | 0.00017 ± 0.00015 | 0.95373 ± 0.01596 | 0.01682 ± 0.00851 | 0.00221 ± 0.00171 | 0.85456 ± 0.07522 |
VotingR2 | 0.00024 ± 0.00011 | 0.00006 ± 0.00003 | 0.99941 ± 0.00067 | 0.00464 ± 0.00375 | 0.00047 ± 0.00064 | 0.95782 ± 0.05873 |
