Structure your answer as a complete ML system design, starting with problem formulation (binary classification with strict temporal constraints), then data collection, feature engineering, modeling, loss, evaluation, and feature selection. Explicitly address the univariate significance test question by explaining why it's unsafe to drop a feature solely based on that test, citing interaction effects and multivariate importance.
Pro tip: Emphasize that the 'moment of release' constraint means you must avoid any post-release data leakage, and that univariate tests can miss features that are only predictive in combination with others—use multivariate feature importance and ablation studies instead.
Define the task as binary classification (shot made/missed) with a strict temporal constraint: only use features available at or before the moment of release. Clarify the prediction point and the target variable.
Gather tracking data (player pose, ball trajectory, shot location, defender distance, game context) from sources like SportVU. Engineer features such as release angle, velocity, player height, distance to hoop, and defensive pressure.
Choose a model (e.g., gradient boosting or logistic regression) and use binary cross-entropy loss. Consider class imbalance and calibration if probabilities are needed.
Evaluate with AUC, log loss, and calibration. For feature selection, use multivariate methods (e.g., permutation importance, SHAP) and ablation studies, not just univariate tests.
Explain that a feature failing a univariate significance test is not safe to drop because it may have interaction effects or be important in the presence of other features. Use multivariate analysis to decide.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.