4

1

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?