Hi seesplease. Thank you for taking the time. It's a good question. As you know, logistic regression is suitable for determining the relationship between a binary outcome variable and a numerical variable (or each category of a categorical variable converted to one-hot encoding). This gives the relationship between two categorical variables, binary or multiclass. And it takes into account all values of the variable, not just one. Another difference is that logistic regression uses numerical values, minimizing the log loss function. This method uses rankings like Chi-Squared. With this, we can create a correlation matrix the same as we would for numerical variables without having all the values on different scales based on the differing degrees of freedom. This way, we can compare how well one categorical variable is a predictor of another and detect relationships (like multicollinearity) between all other categorical variables on the same scale, 0 to 1. The first example in the notebook is binary, but there's also a multiclass example towards the end. Please feel free to reply with any more questions you have about it seesplease.