Reddit Machine Learning Engineer Interview Experience — ML Coding Phone Screen on Imbalanced Data

Had the ML coding phone screen today, and I got a question that was an original question already posted on this forum.

The problem was imbalanced data with 20% positive labels, plus missing data. I was allowed to use Google to look up sklearn functions and syntax. I preprocessed the data first, then built a logistic regression and a random forest.

The follow-up questions were:

• What evaluation metrics would you use for the imbalanced case?
• How would you do feature engineering for a decision tree?
• If there were billions of records, which model would you choose, and how would you do feature engineering for the categorical features?