I know that one of the principal steps within a ML problem is look at the data to gain some insights about it, so to do so, something that could help is exploring the correlation of our features. For that, I know you can use Pearson's correlation coefficient for linear correlation or some other metrics if you're interested in nonlinear dependencies, like Spearman's coefficient.
The problem I've found is that, that is fine when all my features are continous, but what happens if they are not?
If I have in my problem some features that are binary, for example, or even my target variable is binary (a classification problem), which is the correct approach to measure the "correlation" between them and the others? (I think that isn't the appropiate term, but "association", although I'm not sure). The goal is the same, measure which features depends on each other and how much they do it.
And, to complete the question, what happens if the features are not binary but categorical (more than 2 categories)? Is there some specific metric like Pearson's coefficient for that situation, or the standard approach is to encoding them numerically and then apply wichever the solution for the binary case is?
I've look for the answer of these questions before posting them here, but what I've found is several people saying way different methods to do this, so instead of picking one of those and that's it I would like to know first what's the correct approach with this kind of problems.