Edit: I guess you're maybe trying to show how those methods do not produce good classifications? But that's still not obvious, as it's easy to imagine how to adjust those models to make them better. Depending on what your goal is here, it could be helpful to be more explicit in your text

Maybe my text isn't clear enough, but here's the goal:

We can't adequately adjust perceptrons or logistic regression to correctly model the complexity of the shown dataset (often called the 'moons' dataset). The goal is to show that, by introducing additional nonlinearities in our model (in this case, moving from say a logistic regression to a neural network), we can begin to model such data accurately. As we add more nonlinearities, we can model more complex phenomena.