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  3. Machine Learning for Time Series Data in Python

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

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  • 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().