MLS-C01 Modeling Practice Question
During training, a binary classification model has an AUC of 0.99 on the training set but only 0.72 on the validation set. Which of the following is the most likely cause?
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
✓
Overfitting.
A large gap between high training AUC (0.99) and lower validation AUC (0.72) indicates overfitting. Option A is wrong: class imbalance would affect both sets similarly or the model might ignore the minority class, but would not typically produce such a large gap. Option B is wrong: underfitting would show poor performance on both sets (e.g., AUC around 0.5-0.6). Option D is wrong: data leakage would inflate both training and validation metrics, not create a gap.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Class imbalance in the training set.
Why it's wrong here
Class imbalance affects both sets similarly.
- ✗
Underfitting.
Why it's wrong here
Underfitting would yield low AUC on both sets.
- ✓
Overfitting.
Why this is correct
Overfitting results in high training but lower validation AUC.
- ✗
Data leakage from validation to training.
Why it's wrong here
Data leakage would cause both metrics to be high.
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