Practice the judgment, not the answer.
Every question in this bank is exam-style and business-first: framing a prediction, leak-proof features, reading regression output, turning classifier scores into decisions, trees and boosting, and the trust layer — cross-validation, SHAP, conformal intervals, fairness, and forecasting validation. The wrong answers aren't filler; each one is tagged with the specific misconception a smart analyst might hold.
Before you see the answer, you'll be asked to type your reasoning. That's the part that builds the judgment. Take your time — and keep a pencil handy for the numeric ones.
The Prediction Lifecycle
Framing a prediction problem, beating the baseline, splitting honestly, and why accuracy lies.
Data & Leak-Proof Features
Leakage, pipelines, encoding, scaling, and missing data — the work that decides whether a model ships.
Regression & Regularization
Reading coefficients, log targets, ridge and lasso, choosing between RMSE, MAE, and MAPE, and what residuals say.
Classification & Decisions
Odds, confusion matrices, precision and recall, ROC versus PR, thresholds, cost matrices, calibration, and lift.
Trees, Forests & Boosting
Splits and depth, bagging versus boosting, early stopping, feature importance, and where gradient boosting still wins.
Trust & Structure
Cross-validation strategies, SHAP, conformal intervals, fairness slices, k-means, and forecasting validation.