Exercise

# Evaluate the model on a test set

After fitting the model, you can evaluate it on new data. You will give the model a new `X`

matrix (also called test data), allow it to make predictions, and then compare to the known `y`

variable (also called target data).

In this case, you'll use data from the post-season tournament to evaluate your model. The tournament games happen after the regular season games you used to train our model, and are therefore a good evaluation of how well your model performs out-of-sample.

The `games_tourney_test`

DataFrame along with the fitted `model`

object is available in your workspace.

Instructions

**100 XP**

- Assign the test data (
`seed_diff`

column) to`X_test`

. - Assign the target data (
`score_diff`

column) to`y_test`

. - Evaluate the model on
`X_test`

and`y_test`

.