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Exercise

Analyzing metrics per class

While aggregated metrics are useful indicators of the model's performance, it is often informative to look at the metrics per class. This could reveal classes for which the model underperforms.

In this exercise, you will run the evaluation loop again to get our cloud classifier's precision, but this time per-class. Then, you will map these score to the class names to interpret them. As usual, Precision has already been imported for you. Good luck!

Instructions

100 XP
  • Define a precision metric appropriate for per-class results.
  • Calculate the precision per class by finishing the dict comprehension, iterating over the .items() of the .class_to_idx attribute of dataset_test.