An interviewer asks you to build a churn model. What do you do before touching any algorithm?
easyAnswer
- Define the prediction problem precisely: what counts as churn, and over what horizon.
- Fix the prediction time, so every feature is something you would actually have at that moment.
- Then build the dumbest possible baseline: predict the base rate, or a single rule like 'no login in 30 days'.
- That baseline is what every later model must beat, and it takes an hour instead of a week.
- Only then pick a model family, and prefer gradient boosting on tabular data.
- Candidates who jump straight to model choice usually end up with leakage or a target that nobody agreed on.
Check yourself — multiple choice
- Immediately train a deep network
- Pin down the churn definition and horizon, fix the prediction time to avoid leakage, then build a trivial baseline to beat
- Tune hyperparameters first
- Collect as many features as possible
Problem definition, prediction time and a trivial baseline come before any model choice.
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Practise Supervised Learning
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