regression

'Eval is the new system design' for LLM apps, and starting with zero data is the real test. What shows depth is bootstrapping a golden set, choosing task-specific metrics, and running evals continuously as a regression gate.

Alignment that piles on refusals and politeness often quietly erodes reasoning and coding. The alignment tax is real and measurable. Here is how to hold onto the safety gains without paying for them in capability.

Gaussian Processes give predictions with principled uncertainty, which is why they power Bayesian optimization. What matters is the distribution-over-functions intuition, the kernel's role, and naming the O(n cubed) wall. Here is the answer.

The textbook reflex ('drop one') is usually the wrong instinct, and whether collinearity matters at all depends on your model and what you want from it. This is the answer that tells rote apart from real understanding.

Anyone can recite 'linearity and normality.' Few can say which assumption matters for predictions versus inference, how to catch a violation in a residual plot, and what to actually do about it. Here is that answer.

ETA is spatiotemporal regression served in real time over conditions that shift by the minute. The signal is feature freshness, segment or stage decomposition, and the asymmetric cost of being wrong. Here is the design.

Watch time is the label everyone optimizes and hardly anyone measures cleanly. You see minutes only for videos people chose to play, the distribution is savagely skewed, and the slot they saw it in shifted the number. The interview asks whether you can predict a biased label honestly.