Mutual information is a measure of dependence from information theory that quantifies how much the knowledge of one variable reduces the uncertainty about another. For categorical variables, or continuous variables binned into categories, mutual information can be calculated as: $I(U,V) = \sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i \cap V_j |}{N} \log \frac{|U_i \cap V_j |}{|U_i||V_j|}$ where $U$ and $V$ represent the input variables, $U_i$ and $V_j$ represent the categories of the variables, and $N$ is the total number of samples.