Hands-On Unsupervised Learning with Python

Adjusted Mutual Information (AMI) score

The main goal is of this score is to evaluate the level of agreement between Y_true and Y_pred without taking into account the permutations. Such an objective can be measured by employing the information theory concept of Mutual Information (MI); in our case, it's defined as:

The functions are the same as previously defined. When MI → 0, n(i, j) → n_true(i)n_pred(j), whose terms are proportional respectively to p(i, j) and p_true(i)p_pred(j). Hence, this condition is equivalent to saying that Y_true and Y_pred are statistically independent and there's no agreement. On the other side, with some simple manipulations, ...