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How well do you know Rackspace?

Take me through your thought process on leveraging a hash table to solve the two sum problem with a O(n) time complexity.

Can you outline the core principles of Naive Bayes and how you establish a classification cut-off?

Could you elaborate on some methods you've found effective in reducing residuals on statistical models?

Can you explain the X and Y axes of a ROC curve and what they represent?

In your next job at Rackspace, what are you looking for?

How would you design an algorithm to compute the length of a sorted list that includes -1 for out-of-range numbers, without using any built-ins?

Explain the significance of precision and recall in the field of artificial intelligence, and how they can help in improving the accuracy of machine learning models. Also, mention some common limitations and challenges related to the use of these metrics in practice.

Why are you looking for a job at the moment?

Consider drawing from a random variable with a standard normal distribution. How many days do you expect it would take for you to draw a value exceeding 2?