Try fluid.normalize and fluid.standardize on your data. There is an example in LPT and also in the simple learning examples to see their interface. Let us know how you understand them or not.
no, but you might get too much detail to make generalisation. imagine having too much precision on pitch, it won’t help you find trends…
play with it, make music, and that will give you a feel of what works well with each. Then you might have clearer edge cases that help you train and test the mechanism you implement for your specific task.
Maybe @weefuzzy will have more information here, but shooting in the dark is what it’s all about - otherwise you can use tools where someone else have done that experimental training for you, curating the experiments and the results. Either you try to get a feel, or you look at numbers, and you have both possibilities now - you can even look at it with the same paradigm we have in the learning example (json or graphics)
Your next step is to try data sanitisation and normalisation/standardisation and the interaction with the choice of descriptors. There is very little more than learning how they interact since they all fail in various ways.