Comparing RMSE and root-mean-squared Relative Error

In this exercise, you will show that log-transforming a monetary output before modeling improves mean relative error (but increases RMSE) compared to modeling the monetary output directly. You will compare the results of model.log from the previous exercise to a model (model.abs) that directly fits income.

The income_train and income_test datasets are loaded in your workspace, along with your model, model.log.

Also in the workspace:

model.abs: a model that directly fits income to the inputs using the formula

Income2005 ~ Arith + Word + Parag + Math + AFQT

Fill in the blanks to add predictions from the models to income_test.
- Don’t forget to take the exponent of the predictions from model.log to undo the log transform!
Fill in the blanks to gather() the predictions and calculate the residuals and relative error.
Fill in the blanks to calculate the RMSE and relative RMSE for predictions.
- Which model has larger absolute error? Larger relative error?

Exercise

Comparing RMSE and root-mean-squared Relative Error

Instructions