DataCamp

Relative error

In this exercise, you will compare relative error to absolute error. For the purposes of modeling, we will define relative error as

$$ rel = \frac{(pred - y)}{y} $$

that is, the error is relative to the true outcome. You will measure the overall relative error of a model using root mean squared relative error:

$$ rmse_{rel} = \sqrt(\overline{rel^2}) $$

where $\overline{rel^2}$ is the mean of $rel^2$.

The example (toy) dataset fdata is loaded in your workspace. It includes the columns:

y: the true output to be predicted by some model; imagine it is the amount of money a customer will spend on a visit to your store.
pred: the predictions of a model that predicts y.
label: categorical: whether y comes from a population that makes small purchases, or large ones.

You want to know which model does "better": the one predicting the small purchases, or the one predicting large ones.

The data frame fdata is in the workspace.

Fill in the blanks to examine the data. Notice that large purchases tend to be about 100 times larger than small ones.
Fill in the blanks to create error columns:
- Define residual as pred - y.
- Define relative error as residual / y.
Fill in the blanks to calculate and compare RMSE and relative RMSE.
- How do the absolute errors compare? The relative errors?
Examine the plot of predictions versus outcome.
- In your opinion, which model does "better"?