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 a
and 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
-> b
and b
-> a
. Is there anything like this?