Exercise

# Computing log loss with NumPy

To see how the log loss metric handles the trade-off between accuracy and confidence, we will use some sample data generated with NumPy and compute the log loss using the provided function `compute_log_loss()`

, which Peter showed you in the video.

5 one-dimensional numeric arrays simulating different types of predictions have been pre-loaded: `actual_labels`

, `correct_confident`

, `correct_not_confident`

, `wrong_not_confident`

, and `wrong_confident`

.

Your job is to compute the log loss for each sample set provided using the `compute_log_loss(predicted_values, actual_values)`

. It takes the predicted values as the first argument and the actual values as the second argument.

Instructions

**100 XP**

- Using the
`compute_log_loss()`

function, compute the log loss for the following predicted values (in each case, the actual values are contained in`actual_labels`

):`correct_confident`

.`correct_not_confident`

.`wrong_not_confident`

.`wrong_confident`

.`actual_labels`

.