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Minimum Margin

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Active Learning

Difficulty: 4 | Problem written by peter.washington
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The minimum margin of the output probability distribution (the difference between the largest probability in the distribution and the second highest probability in the distributtion) is a common metric for active learning. In each test case for this problem, you will be given a list of numpy arrays representing probability distributions. Your job is to calculate the index of the probability distribution with the minimum margin. (If the first numpy array represents the distribution with the minimum margin, then return 0; if the second numpy array represents the distribution with the minimum margin, then return 1; etc).

 

Sample Input:
<class 'list'>
probability_distributions: [array([0.1 , 0.5 , 0.1 , 0.15, 0.15]), array([0.2, 0.2, 0.2, 0.2, 0.2])]

Expected Output:
<class 'int'>
1

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numpy has been already imported as np (import numpy as np)