How do different models take in account independence/dependence of features?
It depends on the different families of the learning algorithms. For example, Naive Bayes and all the semi-Naive Bayes models assume that the features are indpendent, which is not true in most real world problems. The latter is a disadvantage of Naive Bayes learning algorithm, although in some problems as in natural language process these models usually perform well. Most of the machine learning algorithms do not assume such thing (feature independence). Finally, there is no holistic rule for that.