Fitting models to 2d data
Is the answer already there?



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The function  can capture non-linear interactions between features. You can think of the lattice parameters as the height of poles set in the ground on a regular grid, and the resulting function is like cloth pulled tight against the four poles.

With  features and 2 vertices along each dimension, a regular lattice will have  parameters. To fit a more flexible function, you can specify a finer-grained lattice over the feature space with more vertices along each dimension. Lattice regression functions are continuous and piecewise infinitely differentiable.

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EDIT-

Specifically for these pictures, they have given the example of dateset ranking restaurants(links are below). These graphs shows number of reviews(Y-Axis)/Average rating (x-axis).It has source code also. 
https://www.tensorflow.org/lattice/tutorials/shape_constraints
https://www.tensorflow.org/lattice