model selection

The textbook answer gets a polite nod. The version that names which side of the tradeoff a 6-week customer pilot usually dies on gets the hire signal, here's that version.

The empirical answer for tabular data hasn't changed in a decade, but interviewers want the why, and the two situations where the default is wrong. A decision table you can defend under panel Q&A.

The 2026 FDE differentiator: customers ask for LLMs by name, and the engineer who knows when a $0 logistic regression beats a $40k/month LLM pipeline, with the cost math, wins the room.

The honest version: deep learning learns its own features but is hungry for data and compute, and on a customer's 50,000-row tabular dataset a gradient-boosted tree wins on accuracy, cost, and time-to-ship. The dividing line and the conditions that flip it.

The validation set is the one you tune and select on; the test set is the one you only look at once. Confusing them, or tuning so hard you overfit the validation set, is how a model that aced offline metrics dies in production.

A double-layered probe: your model-selection reasoning, and, quietly, how you handle your employer's confidential information in front of a stranger. Overshare and you fail a test you didn't know was running.