Question Bank
Machine Learning interview questions
Naming a model or a metric is only the start of an interview answer. Explain why it fits the task, and connect training, regularization, validation and evaluation to a concrete example. Start with your own explanation, then state its assumptions and limitations.
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How to reduce the amount of false positives?
What is the computational complexity of this solution?
Does the second solution have the same complexity as a hash map, while the first solution is better in terms of space usage?
Can you write an implementation on the board for better understanding?
What types of documents and their characteristics are used in the system?
What approaches come to mind for improving ranking?
What could a model look like that takes user and item features?
What could be the target variable for model training?
Can you estimate how much time the implementation of such a solution would take?
What approach to data collection and model training?
Is data labeling needed for model training?
What features are useful for training a ranking model?
How to collect statistics for users and items?
What diversity of users and items is needed for training?
What offline metrics to use for model evaluation?
How to measure correlation of metrics with financial results?
What to do if the system returns a million products instead of a hundred?
What user behavior features could be useful?
Can the system solve the task of finding a phone case for a phone added to cart?
How to make the model profit-oriented rather than just sales-oriented?
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