knn

k-NN is quick to code and a solid check of vectorization and the lazy-learner tradeoff. Interviewers look for a tidy vectorized distance calculation plus knowing why naive prediction is O(n) and how to accelerate it. The implementation follows.

Two simple classifiers that keep showing up in interviews because they check whether you grasp assumptions and tradeoffs, not just sklearn calls. What matters is k-NN's laziness and curse of dimensionality against Naive Bayes' independence assumption. Here is the answer.

A million rows does not automatically call for deep learning. The signal is reasoning from dimensionality, data type, inference latency, and label budget, then noting where KNN quietly lives on as approximate nearest neighbor. Here is the answer.

k-NN looks trivial until you ask about picking k, why distances lose meaning in high dimensions, and how to keep prediction fast on millions of points. Here is the tradeoff-aware answer.