A healthcare company is building a model to predict patient readmission within 30 days of discharge. The dataset includes 10,000 patient records with 200 features, including lab results, demographics, and historical admissions. The target variable is highly imbalanced: only 8% of patients are readmitted. The data scientist splits the data into 80% training and 20% test sets, ensuring the same proportion of readmissions in each. The scientist trains a logistic regression model and a random forest model. The logistic regression achieves 92% accuracy but recall of 10% for the readmitted class. The random forest achieves 90% accuracy but recall of 25%. The business requirement is to achieve at least 60% recall for readmissions while maintaining reasonable precision. The scientist also has access to a large collection of unlabeled patient records from other hospitals. Which strategy should the data scientist use to meet the business requirement?

Question

Accepted Answer

Use SMOTE to oversample the minority class in the training set.. Using SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which can improve recall. Option A is wrong because collecting more data may not be feasible and may not help if imbalance persists. Option C is wrong because undersampling reduces data and may lose information. Option D is wrong because changing to a deep learning model may not help with limited data.

Answer

Collect more labeled data from other hospitals.

Answer

Use random undersampling of the majority class in the training set.

Answer

Switch to a deep neural network with more layers.

MLS-C01 Modeling Practice Question

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