Your GMM via EM keeps diverging to infinite likelihood or collapsing clusters. What is going on?
A Gaussian mixture trained by EM has a well-known trap: one Gaussian shrinks onto a single point and the likelihood races to infinity. Understanding the cause, plus the three standard remedies, tells apart people who merely ran sklearn from those who grasp it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A Gaussian mixture trained by EM has a well-known trap: one Gaussian shrinks onto a single point and the likelihood races to infinity. Understanding the cause, plus the three standard remedies, tells apart people who merely ran sklearn from those who grasp it.
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.
