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AIF-C01 Practice Question: A data scientist is evaluating a logistic…
A data scientist is evaluating a logistic regression model for a binary classification task. The model's AUC-ROC score is 0.95 on the training set and 0.51 on the test set. What is the MOST likely issue?
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
✓
The model is overfitting the training data
A large gap between training and test AUC-ROC indicates overfitting — the model memorizes training data but fails to generalize. Cross-validation can help detect and mitigate this. Data leakage or class imbalance could also contribute, but overfitting is the primary symptom.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model is overfitting the training data
Why this is correct
The AUC-ROC gap between training (0.95) and test (0.51) shows the model memorised training data rather than learning generalisable patterns. Near-random test performance confirms overfitting, satisfying the scenario's requirement to identify the most likely issue for this binary classification model.
- ✗
The test set is too small
Why it's wrong here
A small test set widens confidence intervals but cannot produce a 0.44 AUC gap between training and test; that magnitude reflects the model memorising training data. Small test sets are the concern when scores fluctuate across resamples, not when training performance is near-perfect and test performance is near-random.
- ✗
The learning rate is too low
Why it's wrong here
A low learning rate slows convergence, producing mediocre training and test scores together, not a 0.95 training AUC. Learning rate is a gradient-descent hyperparameter; the 0.44 train-test gap indicates variance from overfitting, which regularisation or more data addresses, not learning-rate tuning.
- ✗
The model is underfitting the training data
Why it's wrong here
Underfitting would depress training AUC too, yet the training score is 0.95; the model has clearly fitted the training data. Underfitting is the diagnosis when both training and test scores are low and similar, which is the opposite pattern to the 0.95 versus 0.51 split shown here.
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