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.
Deze oefening maakt deel uit van de cursus
Machine Learning for Time Series Data in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
from sklearn.model_selection import cross_val_score
# Generate scores for each split to see how the model performs over time
scores = cross_val_score(model, X, y, cv=cv, scoring=my_pearsonr)
# Convert to a Pandas Series object
scores_series = pd.Series(scores, index=times_scores, name='score')
# Bootstrap a rolling confidence interval for the mean score
scores_lo = scores_series.____(20).aggregate(partial(____, percentiles=2.5))
scores_hi = scores_series.____(20).aggregate(partial(____, percentiles=97.5))