You're right! Mutual information and the correlation coefficient have distinct interpretations. Unlike the correlation coefficient, mutual information is not limited to capturing linear relationships. Instead, it measures the amount of information that one variable provides about the other variable. It considers all types of dependencies, including non-linear relationships, making it a versatile tool for analyzing complex data. For example, imagine you have two variables, X and Y, where X ranges from 1 to 10 and Y ranges from 0 to 1. Even if X and Y follow a non-linear pattern, mutual information can still capture their dependence accurately.

Yes, mutual information and the correlation coefficient both measure the relationship between variables, but they do so in different ways. While the correlation coefficient quantifies the linear relationship between two variables, mutual information captures any type of dependence between them, whether it is linear or non-linear. In other words, mutual information is not limited to scalar variables and can capture complex relationships that the correlation coefficient might miss.