# How can I use machine learning to predict properties (such as the area) of simple polygons?

Imagine a set of simple (non-self-intersecting) polygons given by the coordinate pairs of their vertices $$[(x_1, y_1), (x_2, y_2), \dots,(x_n, y_n)]$$. The polygons in the set have a different number of vertices.

How can I use machine learning to solve various supervised regression and classification problems for these polygons such as prediction of their areas, perimeters, coordinates of their centroids, whether a polygon is convex, whether its centroid is inside or outside, etc?

Most machine learning algorithms require inputs of the same size but my inputs have a different number of coordinates. This may probably be handled by recurrent neural networks. However, the coordinates of my input vectors can be circularly shifted without changing the meaning of the input. For example, $$[(x_1, y_1), (x_2, y_2),...,(x_n, y_n)]$$ and $$[(x_n, y_n), (x_1, y_1),...,(x_{n-1}, y_{n-1})]$$ represent the same polygon where a starting vertex is chosen differently.

Which machine learning algorithm is both invariant to a circular shifting of its input coordinates and can work with inputs of different sizes?

Intuitively, an algorithm could learn to split each polygon into non-overlapping triangles, calculate areas or perimeters of each triangle, and then aggregate these computations somewhere in the output layer. However, the labels (areas or perimeters) are given only for the whole polygons, not for the triangles. Also, the perimeter of the polygon is not the sum of the perimeters of the triangles. Is thinking about this problem in terms of triangles misleading?

Could you please provide references on machine learning algorithms that solve such tasks? Or any advice, how to approach this task? It does not have to be neural network and does not have to learn exact analytic formulas. Approximate results would be enough.