Society of Actuaries (SOA) PA Practice Exam

What is the purpose of the RMSE function in R?

1. To calculate the sum of residuals

2. To determine the correlation between variables

3. To evaluate the prediction accuracy of a model

4. To fit a linear regression model

The correct answer is: To evaluate the prediction accuracy of a model

The RMSE (Root Mean Squared Error) function in R is specifically designed to evaluate the prediction accuracy of a model by quantifying how much the predictions deviate from the actual observed values. RMSE provides a measure of the average magnitude of the errors in a set of predictions, without considering their direction. It is calculated as the square root of the average of squared differences between the predicted values and the actual values. This metric is particularly valuable in regression analysis, as it gives insights into how well the model is performing—lower RMSE values indicate better model performance. Evaluating model accuracy is essential for assessing how well the model can generalize to new data, which is a critical aspect of the modeling process. In contrast, the other options do not reflect the core function of RMSE. Calculating the sum of residuals, determining the correlation between variables, or fitting a linear regression model are distinct processes that do not directly relate to the assessment of prediction accuracy that RMSE is intended for.