DA0-002 Data Analysis Practice Question
A healthcare analytics team is building a predictive model to identify patients at high risk of readmission within 30 days of discharge. The dataset includes 50,000 patient records with 200 features, including demographics, vital signs, lab results, and historical admissions. The target variable is binary (readmitted or not). The team uses a logistic regression model and achieves an AUC of 0.72 on the test set. However, the model's calibration is poor: for patients predicted to have a 70% risk, the actual readmission rate is only 40%. The team wants to improve calibration without significantly reducing discrimination (AUC). The data scientist suggests applying Platt scaling. However, the team lead is concerned that Platt scaling may reduce the model's ability to rank patients correctly. Which of the following is the best course of action?
⚠ Common exam trap
Many candidates think Platt scaling changes the model's ranking (AUC), but in reality it applies a monotonic transformation that preserves rank order, so discrimination is unaffected.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.
Platt scaling is a post-processing technique that fits a logistic regression model on the predicted probabilities from the original model using a held-out validation set. This recalibrates the probabilities without altering the ranking of patients (the AUC remains unchanged), directly addressing the poor calibration while preserving discrimination. Option C correctly describes this procedure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.
Why it's wrong here
Discarding data reduces sample size and may introduce bias.
- ✗
Ignore calibration because AUC is the only metric that matters for readmission risk models.
Why it's wrong here
Calibration is important for decision-making; AUC alone can be misleading.
- ✓
Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.
Why this is correct
Platt scaling is designed to improve calibration while maintaining AUC.
- ✗
Switch to a random forest model, which inherently produces better-calibrated probabilities.
Why it's wrong here
Random forests often have better calibration but may not necessarily preserve AUC; also, this changes the model entirely.
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