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 & 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 & 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!