Learn how each ML classifier works: decision boundary vs. assumed true boundary

In the latest post of my own blog, I argued about how to learn how each machine learning classifier works visually. My idea is that first I prepare samples for training and then I show its assumed true boundary, and finally decision boundary estimated by the classifier with a dense grid covering over the space as test dataset and the assumed boundary are compared. In the case below, the assumed true boundary of the space is a set of 3 parallel lines; I think everybody will guess so intuitively, but the most important point here is whether any machine learning classifier works so. For example, when multinomial logit - one of linear classifiers - is trained by samples below, it gives decision boundary for a grid dataset covering the whole space. It looks almost the same as the assumed boundary.