This aggregated prediction makes outliers less influential on the model and improves the reliability of RF against noises in the data. However, similar with other machine learning model, the process of big data in RF usually requires an excessive computational cost due to the extensive training on each tree and predictions on forest with a lot of trees. Additionally, compared to PR and BPNN, the ensemble process -where multiple decision trees contribute to the outcome -makes the underlying logic of individual predictions and their integration difficult to interpret, posing significant challenges for explainability.