Mutual information is a quantity measuring dependence in information theory, basing itself on how the knowledge of one variable reduces the uncertainty on the other variable. In our case, this quantity can be expressed 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 $U$, $V$ represent the input variables and $U_i$ , $V_j$ represent the categories of the variables.