"For most models, there will be associated parameters. These are the things that we use the data to decide on. Parameters in a decision tree include: the specific questions we asked, the order in which we asked them, and the classification decisions at the leaves."
My question is about the first sentence. Is there any model in machine learning that does not have parameters? I can't think of any. For sure there are models without hyperparameters (for instance, the linear model does not contain any hyperparameter, but it still contains 2 parameters, the slope and the y-intercept).
If such parameterless models exist, what are their purpose then? Isn't it the whole point of training to tune a model's parameters?