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!

Este ejercicio forma parte del curso

Machine Learning for Finance in Python

Ver curso

Instrucciones del ejercicio

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'.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# 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()

Editar y ejecutar código