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Design classification under missingness and imbalance
Designing a 30-Day Readmission Classifier: An End-to-End Machine Learning Plan
Context: You are tasked with building a binary classifier to predict 30-day patient readmission using a combination of claims and Electronic Health Record (EHR) features. The dataset presents two significant challenges:
- Approximately 30% of feature values are missing, with potential concentrations by column or row.
- The positive rate (patients readmitted within 30 days) is low, at 6%, indicating a class imbalance problem.
Your objective is to describe a concrete, defensible end-to-end plan that addresses the following aspects, including justifications for your choices and an analysis of associated trade-offs:
1. Missing Data Handling
- Diagnose the types of missingness (MCAR, MAR, MNAR) and distinguish between row-wise and column-wise missingness.
- Formulate a decision strategy for when to drop rows or columns versus when to impute missing values.
- Specify at least two distinct imputation strategies (e.g., simple, model-based, indicator augmentation) and detail how you would validate their effectiveness within a machine learning pipeline without introducing data leakage.
2. Classification Measurement & Evaluation
- Define the following classification metrics: accuracy, precision, recall, specificity, F1-score, ROC-AUC, PR-AUC, and calibration.
- Provide one real-world example where recall is the primary priority, explaining your reasoning. Similarly, provide another real-world example where precision is the primary priority, with justification.
- Explain how factors such as threshold choice, cost matrices, or utility curves influence the selection and interpretation of evaluation metrics.
3. Addressing Class Imbalance (6% Positives)
- Propose at least three distinct approaches to mitigate the class imbalance problem (e.g., class weights, resampling techniques like SMOTE/ADASYN/undersampling, threshold moving, focal loss).
- Explain how your evaluation methodology would need to change (e.g., using PR curves, stratified cross-validation, grouped cross-validation by patient) to avoid optimistic bias in performance estimates.
4. Enhancing Logistic Regression Performance
- List specific feature engineering and modeling steps to improve a logistic regression model. This should include considerations for regularization choices and tuning, interaction terms, nonlinearity (e.g., splines), monotonic constraints where applicable, calibration methods (e.g., Platt scaling, Isotonic regression), handling collinearity, and robust standardization techniques.
- Describe how you would conduct ablation studies and quantify the performance lift achieved by your proposed enhancements compared to a baseline model.
- Be precise about your protocols for preventing data leakage, conducting hyperparameter tuning, and effectively presenting the model's results and insights to non-technical stakeholders.
AI interview prep
Core dilemma
Building a robust, interpretable, and fair predictive model for a critical healthcare outcome (patient readmission) under significant data quality challenges (high missingness, severe class imbalance) while ensuring actionable insights and effective communication with non-technical stakeholders.
Mid-level signals3
Senior signals3
Red flags3
3 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design classification under missingness and imbalance
Designing a 30-Day Readmission Classifier: An End-to-End Machine Learning Plan
Context: You are tasked with building a binary classifier to predict 30-day patient readmission using a combination of claims and Electronic Health Record (EHR) features. The dataset presents two significant challenges:
- Approximately 30% of feature values are missing, with potential concentrations by column or row.
- The positive rate (patients readmitted within 30 days) is low, at 6%, indicating a class imbalance problem.
Your objective is to describe a concrete, defensible end-to-end plan that addresses the following aspects, including justifications for your choices and an analysis of associated trade-offs:
1. Missing Data Handling
- Diagnose the types of missingness (MCAR, MAR, MNAR) and distinguish between row-wise and column-wise missingness.
- Formulate a decision strategy for when to drop rows or columns versus when to impute missing values.
- Specify at least two distinct imputation strategies (e.g., simple, model-based, indicator augmentation) and detail how you would validate their effectiveness within a machine learning pipeline without introducing data leakage.
2. Classification Measurement & Evaluation
- Define the following classification metrics: accuracy, precision, recall, specificity, F1-score, ROC-AUC, PR-AUC, and calibration.
- Provide one real-world example where recall is the primary priority, explaining your reasoning. Similarly, provide another real-world example where precision is the primary priority, with justification.
- Explain how factors such as threshold choice, cost matrices, or utility curves influence the selection and interpretation of evaluation metrics.
3. Addressing Class Imbalance (6% Positives)
- Propose at least three distinct approaches to mitigate the class imbalance problem (e.g., class weights, resampling techniques like SMOTE/ADASYN/undersampling, threshold moving, focal loss).
- Explain how your evaluation methodology would need to change (e.g., using PR curves, stratified cross-validation, grouped cross-validation by patient) to avoid optimistic bias in performance estimates.
4. Enhancing Logistic Regression Performance
- List specific feature engineering and modeling steps to improve a logistic regression model. This should include considerations for regularization choices and tuning, interaction terms, nonlinearity (e.g., splines), monotonic constraints where applicable, calibration methods (e.g., Platt scaling, Isotonic regression), handling collinearity, and robust standardization techniques.
- Describe how you would conduct ablation studies and quantify the performance lift achieved by your proposed enhancements compared to a baseline model.
- Be precise about your protocols for preventing data leakage, conducting hyperparameter tuning, and effectively presenting the model's results and insights to non-technical stakeholders.