Vapnik-Chervonenkis Dimension: why cannot four points on a line be shattered by rectangles? So I'm reading "Introduction to Machine Learning" 2nd edition, by Bishop, et. all.  On page 27 they discuss the Vapnik-Chervonenkis Dimension which is,


  "The maximum number of points that can be shattered by H [the hypothesis class] is called the Vapnik-Chervonenkis (VC) Dimension of H, is denoted VC(H) and measures the capacity of H."


Whereas "shatters" indicates a hypothesis $h \in H$ for a set of N data points such that it separates the positive examples from the negative.  In such an example it is said that "H shatters N points".

So far I think I understand this.  However, the authors lose me with the following:


  "For example, four points on a line cannot be shattered by rectangles."


There must be some concept here I'm not fully understanding, because I cannot understand why this is the case.  Can anyone explain this to me?
The definition of "a set $P$ can be shattered by rectangles" is that for every subset of $P$, there is a rectangle that contains precisely that subset and excludes the rest of $P$. Equivalently, every labeling of the points as positive and negative is consistent with at least one hypothesis in $H$.

Now consider four points $p,q,r,s$ along a line in the plane.  Since there is no rectangle that contains $p$ and $r$ but excludes $q$ and $s$, these four points cannot be shattered by rectangles.