See how the ensemble performed

Let's check performance of our ensembled model to see how it's doing. We should see roughly an average of the R\(^2\) scores, as well as a scatter plot that is a mix of our previous models' predictions. The bow-tie shape from the custom loss function model should still be a bit visible, but the edges near x=0 should be softer.

This exercise is part of the course

Machine Learning for Finance in Python

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Exercise instructions

Evaluate the R\(^2\) scores on the train and test sets. Use the sklearn r2_score() function (already imported for you) with train_targets and train_preds from the previous exercise.
Plot the train and test predictions versus the actual values with plt.scatter().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from sklearn.metrics import r2_score

# Evaluate the R^2 scores
print(r2_score(____, ____))
print(r2_score(test_targets, test_preds))

# Scatter the predictions vs actual -- this one is interesting!
plt.scatter(____, ____, ____)
plt.scatter(test_preds, test_targets, label='test')
plt.legend(); plt.show()

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