| Â | MAPE | SMAPE | RRSE | RMSE |
|---|
Linear | 0.61 | 0.135 | 0.855 | 4.172 |
RF | 0.599 | 0.131 | 0.851 | 4.153 |
SGB | 0.606 | 0.126 | 0.852 | 4.159 |
NB | 0.599 | 0.124 | 0.82 | 4.003 |
XGBoost | 0.439 | 0.113 | 0.697 | 3.403 |
- Data showed as mean; RF Random forest, SGB Stochastic gradient boosting, NB Naïve Bayes classifier, XGBoost eXtreme gradient boosting, MAPE Mean absolute percentage error, SMAPE Symmetric MAPE, RAE Relative absolute error, RRSE Root relative squared error, RMSE Root mean square error. The errors were used to compare the accuracies of the models. The smaller the errors, the better the model was
