Penalizing highly confident wrong answers

As Peter explained in the video, log loss provides a steep penalty for predictions that are both wrong and confident, i.e., a high probability is assigned to the incorrect class.

Suppose you have the following 3 examples:

$$A: y=1, p=0.85 $$

$$B: y=0, p=0.99 $$

$$C: y=0, p=0.51 $$

Select the ordering of the examples which corresponds to the lowest to highest log loss scores. y is an indicator of whether the example was classified correctly. You shouldn't need to crunch any numbers!

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