MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training, the model achieves an R² of 0.85 on the training set but only 0.60 on the test set. Which of the following is the MOST likely cause of this discrepancy?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between overfitting and multicollinearity, where candidates mistakenly attribute a training-test R² gap to multicollinearity instead of recognizing it as a generalization failure.
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 high R² on the training set (0.85) paired with a significantly lower R² on the test set (0.60) is a classic symptom of overfitting. The model has learned noise and specific patterns in the training data that do not generalize to unseen data, causing poor test performance. Regularization techniques like Lasso or Ridge, or reducing model complexity, would typically address this issue.
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
Overfitting causes high training performance but poor generalization to test data.
- ✗
There is multicollinearity among the features
Why it's wrong here
Multicollinearity affects coefficient stability but does not directly cause a large train-test R² gap.
- ✗
The model is underfitting the training data
Why it's wrong here
Underfitting would result in low R² on both training and test sets.
- ✗
There is data leakage between the training and test sets
Why it's wrong here
Data leakage typically inflates test performance, not the gap.
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