So, how well did we do? How do we actually measure how well we did? It all depends on the situation.
Let's evaluate our model by plotting the error counts:
error = test_predictions - test_labels
plt.hist(error, bins = 25)
plt.xlabel("Prediction Error [MPG]")
_ = plt.ylabel("Count")
plt.show()
Now, let's view the output:
It looks like the model predicted reasonably well. The distribution error of the model shows it is not quite Gaussian or normally distributed, but we can expect non-Gaussian as the number of samples is very small.