Hi, I got the “All tests passed” on this exercise, but I would still like to understand: In order to find the accuracy, why were we asked to first find “error”, the average of the absolute values of the differences between y_hats and test_y (I believe we must go over each pair of label-prediction with a loop) and then do *1 - error*?

Wouldn’t it be shorter to change *y_hats* and *test_y* to np.arrays, then do **sum(y_hats == test_y)** and divide the result by len(y_hats)?

We learned the neat method of comparing two 1-dimensional arrays on week one’s notebook. Is there any special benefit in doing like the notebook is asking now?