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!
Questo esercizio fa parte del corso
Machine Learning for Finance in Python
Istruzioni dell'esercizio
- Create predictions on the test set with
.predict(),model_2, andscaled_test_features. - Evaluate the R\(^2\) score on the test set predictions using
test_predsandtest_targets. - Plot the test set targets vs actual values with
plt.scatter(), and label it'test'.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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()