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I'm looking for a metric that can be used to quantify how imbalanced the labels are in a dataset.
I'm not looking for a strategy to solve the imbalance problem, I just want to present how imbalanced my dataset is. I've computed the ratio of the most frequent and least frequent labels which is probably an ok way of doing it but I'm sure there's a more robust way?
You are looking for Entropy. The higher the entropy, the more imbalanced it is. You can use this function for calculating it.
I'd recommend looking at the Gini index as a measure of the inequality in the class sizes. Unlike entropy or standard deviation, Gini index is explicitly designed to capture the amount of inequality in a distribution.
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I'm looking for a metric that can be used to quantify how imbalanced the labels are in a dataset.
I'm not looking for a strategy to solve the imbalance problem, I just want to present how imbalanced my dataset is. I've computed the ratio of the most frequent and least frequent labels which is probably an ok way of doing it but I'm sure there's a more robust way?
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You are looking for Entropy. The higher the entropy, the more imbalanced it is. You can use this function for calculating it.
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entropy(pk)where pk are the frequencies of each class - if my data is distributed Ci=[100,10,1] then pk would be [100/111,10/111,1/111]? (I understand the function accepts unnormalised data) - David Waterworth Feb 25 at 21:10