What is the primary purpose of the Root Mean Square Error (RMSE) in machine learning?

The Root Mean Square Error (RMSE) is primarily used to assess the predictive power of a model. It quantifies the difference between the values predicted by the model and the actual values observed in the data. By calculating RMSE, one can understand how well the model performs; lower RMSE values indicate better predictive accuracy, as they represent smaller deviations between the predicted and observed data points.

In the context of model evaluation, RMSE serves as an important metric for comparison between different models or for tuning hyperparameters. It provides a clear and interpretable numerical assessment of error, which can be particularly useful when trying to improve a model's performance or when conveying results to stakeholders who may not have a technical background in machine learning.

Other options do not align with the primary use of RMSE as effectively. For example, measuring training time is unrelated to the effectiveness of the model's predictions. Evaluating model complexity focuses on aspects like the number of parameters or how flexible a model is, rather than its predictive capability. Lastly, measuring data encoding techniques concerns the preprocessing steps of the data rather than the model's predictive accuracy.