Visualize the results

We've fit our model with the custom loss function, and it's time to see how it is performing. We'll check the R\(^2\) values again with sklearn's r2_score() function, and we'll create a scatter plot of predictions versus actual values with plt.scatter(). This will yield some interesting results!

Cet exercice fait partie du cours

<cours>Machine Learning for Finance in Python</cours>

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Instructions de l’exercice

Create predictions on the test set with .predict(), model_2, and scaled_test_features.
Evaluate the R\(^2\) score on the test set predictions using test_preds and test_targets.
Plot the test set targets vs actual values with plt.scatter(), and label it 'test'.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Evaluate R^2 scores
train_preds = model_2.predict(scaled_train_features)
test_preds = ____
print(r2_score(train_targets, train_preds))
print(____)

# Scatter the predictions vs actual -- this one is interesting!
plt.scatter(train_preds, train_targets, label='train')
plt.scatter(____)  # plot test set
plt.legend(); plt.show()

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