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What can you tell me about the efficiency of SVM training in terms of time complexity? Could you explain the time efficiency of inference with a trained SVM model? Could you describe how kernel selection (Linear vs RBF) affects the time complexity of SVMs?

How do you earn the trust of your team?

What approach do you take to handle datasets with imbalanced classes, where one category has a much larger number of instances than the other?

How to decompose errors into variance and bias?

How do you ascertain the longest substring devoid of repeating characters within a string?

If you had a chance to guess a randomly selected card from a deck, what's your probability of getting it right? And if you could ask if it's red or a face card to better your odds, which question would you pose?

Please share your most innovative idea.

Describe any hurdles you have overcome while working with supervised machine learning, and how did you overcome them?

Can you talk about the complexities involved in training deep neural networks with an extensive number of layers?

What criteria would you use to determine whether monthly retention is improving?