Exercise

# Measure model fit

Now you will measure the regression performance on both training and testing data with two metrics - root mean squared error and mean absolute error. This is a critical step where you are measuring how "close" are the model predictions compared to actual values.

The `numpy`

library has been loaded as `np`

. The `mean_absolute_error`

and `mean_squared_error`

functions have been loaded. The training and testing target variables are loaded as `train_Y`

and `test_Y`

, and the **predicted** training and testing values are imported as `train_pred_Y`

and `test_pred_Y`

respectively.

Instructions

**100 XP**

- Calculate the root mean squared error on the training data by using the
`np.sqrt()`

function. - Calculate the mean absolute error on the training data.
- Calculate the root mean squared error on the testing data.
- Calculate the mean absolute error on the testing data.