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.

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.

Exercise

Computing log loss with NumPy

Instructions