I am currently working on prediction of UV-vis spectra from structure of molecules. I have read multiple papers where structure descriptors were used as inputs for machine learning to find various properties. I have also come across different types of structre descriptors such as the adjacency matrix.
However, I haven't been able to find much information on how exactly these descriptors are fed into the statistical model. I want to understand how the descriptors are interpreted by the software algorithm. For example, if I have an adjacency matrix, how should I put it into a software like scikit-learn? I am looking for a beginner's level explanation of the descriptors and their interpretation by software (not just adjacency matrices, other types of descriptors too, like MOE, 3D descriptors, fingerprints etc.).
I am using machine learning to some extent, but would like to know about applying regression based methods on descriptors because I want to understand what actually is happening under the hood.