What are Machine learning model characteristics? This question I have received in some Machine Learning related interview and Here is the question 


  What questions would you ask to learn about machine learning model characteristics? 


This is what I think:

I did a bit research on the internet &amp; found this resource, but still not very clear about How ML model characteristics are equivalent to asking trade-off between different algorithms in supervised learning settings. What I understood and framed my answer is:


First, for simplicity, I assumed that this model is used for some supervised learning task(classification/ regression) then,


I would first try to find a learning algorithm used to create this model, because this will provide me with a clue of the created model and help me to talk about different issues within it(such as, 


How complex or simple your model is(feature engineering!)? 
What are your training &amp; test errors(Model performance)



Both will help me to talk about the bias vs variance trade-off.
Furthermore, I could talk about different learning algorithm trade-offs in supervised settings.


Is the model based on identifying correlations i.e output variable can be expressed in terms of the linear/non-linear combination of features?
(targeting linear regression, logistic regression, SVM or Neural network algorithms, or
it's decision tree-based algorithms.



I am not confident enough that my answer is complete or correct (since I am not able to understand what precisely characteristics of a model are?), so I need this community's help to provide feedback! please feel free to add your suggestion to it!
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.