Root Mean Square Error (RMSE) is a widely used metric for evaluating model prediction accuracy.
A smaller RMSE signifies closer agreement between predicted and actual values, indicating higher model accuracy.
The acceptable RMSE threshold is context-dependent, varying with the dataset.
However, an RMSE below 180 is generally considered acceptable for effective models in practical applications.
Higher RMSE values denote greater errors, suggesting the model requires optimization or adjustments.
Model performance can be enhanced through improved data preprocessing, parameter tuning, or algorithm selection.
Therefore, the acceptable threshold is 180, corresponding to option (C).