Exercise

# Regression evaluation

The `test_set`

and `model`

objects that you have derived in the previous exercise are available in your environment.

It's useful to present the accuracy of predictions with one number. You can then easily compare several models and show the progress to your employer or future employer.

**Root Mean Squared Error** and **Mean Absolute Error** are widely used to evaluate the regression models. Recall that their formulas are:

\(RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n}(y_i - \hat{y}_i)^2}\)

\(MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|\)

Instructions 1/2

**undefined XP**

- Assign the
`Hwt`

variable from the test set to`y`

. - Calculate the predictions using the test set and assign them to
`y_hat`

. - Assign the number of rows of the test set to
`n`

.