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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
High training performance and poor test performance is classic overfitting.
- ✗
The test set is too small
Why it's wrong here
While a small test set can increase variance, the drastic drop indicates overfitting.
- ✗
The learning rate is too low
Why it's wrong here
Learning rate affects convergence speed but not such a discrepancy.
- ✗
The model is underfitting the training data
Why it's wrong here
Underfitting would show low accuracy on both sets, not a large gap.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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