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I have a dataset containing independent variables as three different 3-D coordinates. For reference, the data is structured like this

Independent 1: (1,2,3) , (4,5,6)... Independent 2: (7,8,9), (10,11,12)... Independent 3: (13,14,15), (16,17,18)...

Dependent: 0.5, 0.6...

Does anyone have any suggestions for ML/Regression techniques to apply to this data? So far, I have tried creating new features off these coordinates (distances between the three x-y-z coordinates, area of the triangle in forms...). Nothing seems to give high accuracy.

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This depends a lot on the context of what problem you're trying to solve. For example, in the context of a triangular mesh, additional interesting features might be the normal vector to the plane defined by the 3 points, and the distance of that plane to the origin. If this is some sort of geometry problem, you might care about the cross-products and dot-products between each pair of points.

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