I use Cramer's V to calculate correlation of features in a dataset made of only nominal features.
Let's consider the following dataset:
a | b -------- 0 | 0 0 | 1 0 | 0 1 | 2 1 | 2 1 | 3
Calculating Cramer's V for features
b yields 0.707. Since it's symmetric, there's information loss in this case - as we can see, knowing the value of
b means we know for sure what is the value of
a, but this is no the case if we are given the value of
a; in this case, the number possible values of
b decreases, but it's still not known for sure.
I'd like to find an asymmetric metric that will provide this information for nominal values - meaning, will give a different value when calculated
a. Is there anything like this?