AI0-001 Machine Learning and Deep Learning Practice Question
A hospital wants to deploy a machine learning model to predict patient readmission risk within 30 days. They have a dataset with 10,000 records, 70 features including demographics, lab results, and past admissions. The target variable is binary (readmitted or not). The data scientist trains a logistic regression model and achieves an AUC of 0.85 on the test set. However, the hospital's clinicians require interpretability of predictions to trust the model. Which action should the data scientist take to ensure the model meets the interpretability requirement while maintaining performance?
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
✓
Use the logistic regression model as is, since it is inherently interpretable with coefficients
(PCA + logistic regression) reduces dimensionality but loses interpretability and may degrade performance. Option B (random forest with feature importance) is less interpretable than logistic regression. Option C (deep neural network with LIME/SHAP) adds complexity and may reduce transparency. Option D (keep logistic regression) provides inherent interpretability through coefficients, meeting the requirement without sacrificing performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of features to 10 using PCA and retrain the logistic regression
Why it's wrong here
PCA reduces dimensionality but loses feature interpretability and may hurt performance.
- ✗
Replace logistic regression with a random forest model and use feature importance plots
Why it's wrong here
Random forest is an ensemble and less interpretable than logistic regression.
- ✗
Train a deep neural network and apply LIME or SHAP for explanations
Why it's wrong here
Deep networks are black boxes; LIME/SHAP add explanations but reduce inherent interpretability.
- ✓
Use the logistic regression model as is, since it is inherently interpretable with coefficients
Why this is correct
Logistic regression coefficients provide direct interpretability for each feature.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.