Measure performance

Now that we've fit our neural net, let's check performance to see how well our model is predicting new values. There's not a built-in .score() method like with sklearn models, so we'll use the r2_score() function from sklearn.metrics. This calculates the R\(^2\) score given arguments (y_true, y_predicted). We'll also plot our predictions versus actual values again. This will yield some interesting results soon (once we implement our own custom loss function).

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Machine Learning for Finance in Python

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Instrucciones del ejercicio

Obtain predictions from model_1 on the scaled test set data (scaled_test_features and test_targets).
Print the R\(^2\) score on the test set (test_targets and test_preds).
Plot the test_preds versus test_targets in a scatter plot with plt.scatter().

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from sklearn.metrics import r2_score

# Calculate R^2 score
train_preds = model_1.predict(scaled_train_features)
test_preds = model_1.predict(____)
print(r2_score(train_targets, train_preds))
print(r2_score(____, ____))

# Plot predictions vs actual
plt.scatter(train_preds, train_targets, label='train')
plt.scatter(____)
plt.legend()
plt.show()

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