In every example I see(spam, negative vs positive tweet , weather study...) there is always the assumption that the input features (or variables) are independent.
In order for me to be able to understand whether naive Bayes can be applied to my problem, I need to truly understand what dependent variables means. I know what dependent variables mean in mathematics but I can't seem to be able to grasp the notion in this sense because I've never come across a case study where the person says that they have dependent features so they can't use a particular data mining algorithm.
My main confusion is due to the fact that in my mind, all features are connected somehow(and I picture them as being dependent) for example in the weather case, if we choose humidity, temperature and wind as inputs aren't they related to a certain degree? like if it's really windy the temperature usually drops. I feel like maybe I'm misunderstanding dependency when it comes to data mining algorithms.
Can someone provide me with an example in which the independent features assumption can't apply?