Answer core ML fundamentals questions
Company: Pinterest
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: hard
Interview Round: Online Assessment
You are asked several short ML fundamentals questions:
1) Define **precision** and **recall** for a binary classifier and explain how they relate to a confusion matrix.
2) Given a confusion matrix (TP, FP, TN, FN), compute precision and recall and explain what types of errors increase/decrease each metric.
3) Answer basic questions about **gradients** (e.g., what a gradient represents, and how it is used in gradient descent).
4) Discuss common **ensemble learning tradeoffs** (e.g., bagging vs boosting, bias–variance, compute/latency).
5) Do a simple **neural network forward pass by hand** for a small feed-forward network (matrix multiply + bias + activation) and produce the final output.
Overview: This question evaluates a candidate's mastery of core Machine Learning fundamentals, including classification metrics (precision and recall) with confusion-matrix interpretation, gradient concepts for optimization, ensemble learning tradeoffs, and the mechanics of a neural network forward pass.
Read the full Pinterest Machine Learning Engineer interview experience this question came from