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Comparing the RMSE of two models

As you did using the sum of squared residuals and \(R^2\), let's once again assess and compare the quality of your two models using the root mean squared error (RMSE). Note that RMSE is more typically used in prediction settings than explanatory settings.

model_price_2 and model_price_4 are available in your workspace.

This exercise is part of the course

Modeling with Data in the Tidyverse

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# MSE and RMSE for model_price_2
get_regression_points(model_price_2) %>%
  mutate(sq_residuals = residual^2) %>%
  summarize(mse = mean(sq_residuals), rmse = sqrt(mean(sq_residuals)))

# MSE and RMSE for model_price_4
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