Exercise

# Visualizing model score variability over time

Now that you've assessed the variability of each coefficient, let's do the same for the performance (scores) of the model. Recall that the `TimeSeriesSplit`

object will use successively-later indices for each test set. This means that you can treat the *scores* of your validation as a time series. You can visualize this over time in order to see how the model's performance changes over time.

An instance of the Linear regression model object is stored in `model`

, a cross-validation object in `cv`

, and data in `X`

and `y`

.

Instructions 1/2

**undefined XP**

- Calculate the cross-validated scores of the model on the data (using a custom scorer we defined for you,
`my_pearsonr`

along with`cross_val_score`

). - Convert the output scores into a pandas Series so that you can treat it as a time series.
- Bootstrap a rolling confidence interval for the mean score using
`bootstrap_interval()`

.