Mutual information matrix python

1. What is mutual information in Python?
2. What is mutual information matrix?
3. How do you calculate mutual information?
4. How do you interpret mutual information score?

What is mutual information in Python?

Mutual information (MI) [1] between two random variables is a non-negative value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency.

What is mutual information matrix?

Mutual information is a metric from information theory that quantifies the non-linear co-dependencies between a pair of random variables. When extended to more than one pair, a mutual information matrix can be constructed, similar to the correlation matrix.

How do you calculate mutual information?

The mutual information can also be calculated as the KL divergence between the joint probability distribution and the product of the marginal probabilities for each variable. — Page 57, Pattern Recognition and Machine Learning, 2006. This can be stated formally as follows: I(X ; Y) = KL(p(X, Y) || p(X) * p(Y))

How do you interpret mutual information score?

The MI score will fall in the range from 0 to ∞. The higher value, the closer connection between this feature and the target, which suggests that we should put this feature in the training dataset. If the MI score is 0 or very low like 0.01. the low score suggests a weak connection between this feature and the target.