What is Linear Discriminant Analysis (LDA), and how does it differ from PCA?
LDA is the supervised cousin of PCA, and the contrast is a favorite. What matters is knowing that LDA uses the labels to maximize class separation while PCA only chases variance, plus the C minus 1 dimension cap that trips up most candidates.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
LDA is the supervised cousin of PCA, and the contrast is a favorite. What matters is knowing that LDA uses the labels to maximize class separation while PCA only chases variance, plus the C minus 1 dimension cap that trips up most candidates.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
