Exercise

# Evaluate the optimal forest

In this last exercise of the course, you'll evaluate the test set RMSE of `grid_rf`

's optimal model.

The dataset is already loaded and processed for you and is split into 80% train and 20% test. In your environment are available `X_test`

, `y_test`

and the function `mean_squared_error`

from `sklearn.metrics`

under the alias `MSE`

. In addition, we have also loaded the trained `GridSearchCV`

object `grid_rf`

that you instantiated in the previous exercise. Note that `grid_rf`

was trained as follows:

```
grid_rf.fit(X_train, y_train)
```

Instructions

**100 XP**

Import

`mean_squared_error`

as`MSE`

from`sklearn.metrics`

.Extract the best estimator from

`grid_rf`

and assign it to`best_model`

.Predict

`best_model`

's test set labels and assign the result to`y_pred`

.Compute

`best_model`

's test set RMSE.