I think it's great, surprisingly good at separating what I enter to return a response.
however, it has the mindset of an exam marker, not an interview. Let me elaborate - I listed 4 things as possible ways to optimise a machine learning model
The feedback for "How would you optimise a machine learning model" is
"You:
optimise the parameters; look at bias needs; can it be put into production? run test runs on small subset of the data first
Interviewer:
Your answer is a good start, but it could be more specific. Here are some tips to improve your answer: - When you say "optimize the parameters", make sure to clarify which parameters you mean. Are you referring to hyperparameters or model parameters? How would you go about tuning them? - Regarding bias, it's important to check for both bias and variance. How would you assess whether your model is suffering from high variance or bias? What steps would you take to address these issues? - When you say "put into production", this implies that you have a specific deployment strategy in mind. Can you elaborate on what that strategy might look like? How would you ensure that your model is scalable, reliable, and maintainable? - Running tests on a small subset of the data is a good idea, but how would you choose which subset to use? What metrics would you use to evaluate your model's performance? How would you know when you've achieved the desired level of performance?"
The last point is important, the point on subsets is the kind of question I would ask to test train/test/validation set and cross validation knowledge without prompting those terms.
But the first points are not necessarily a good idea. If a candidate went into so much detail on one point, they're not hitting the mark on succinctly and clearly communicating how they'd optimise a model. That should be for follow up questions.
That said, despite that the response is not interview like (let's say, an interview would just follow up with one question, not many), if I was warming up my skills for a job this would be a godsend, because it mentions all kinds of things that would catch me out in an interview... not sure I would actually learn them though, more learn the appearance of knowing them, which is an ironic reflection on ChatGPT itself...