curse of dimensionality

Dimensionality questions probe linear-algebra intuition and practical judgment together. What interviewers reward is what PCA really does (project onto max-variance directions), why high dimensions hurt, and the honest catch: it is unsupervised, so it can discard the exact direction your label needs. Here is the answer.

High-dimensional data breaks the intuitions and methods that work in two or three dimensions. What matters is naming the concrete effects (distances concentrate, data goes sparse, overfitting climbs) and the mitigations that actually move the needle.

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.