What are Machine learning model characteristics?
I am using neural networks in industrial fault diagnosis and dynamic system modeling, so I would consider:

is it a static or dynamic system? Depending on it you may choose other network structure (for example RNN for dynamic systems)

what is the quality of quantity data? Depending on it you may choose training algorithm (maybe bayesian regularization for noisy data). SVR modeling would be good for high-quality data with a lots of samples. It would be way faster learning than neural networks,

are there any delays in the system? Sometimes performance might be improved by adding delays to model. Cross-correlation might be applied to check optimal delays or diagnose some echos in signals

sometimes correlation analysis could reduce dimimensionality of a problem. Computing correlations might help selecting inputs of the model. Sometimes PCA is a nice idea to get rid of curse of dimensionality.
But depending on field of use those questions could be different.