Katkat
Back

Explain ML model fundamentals

GoogleMachine Learning Engineer · Onsite
HardMachine Learning EngineerOnsiteML System Design

Last updated

Explain ML model fundamentals evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Question

Explain the principles and assumptions behind logistic regression. How does Naive Bayes work and when does it perform well? Describe the transformer architecture and why self-attention helps. What metrics would you use to evaluate a multi-class classification model and why? Compare bagging and boosting: how do they reduce error?