MLS-C01 Modeling Practice Question
A data scientist is evaluating a regression model. The RMSE on the training set is 2.5, and on the test set is 2.7. The R² on the test set is 0.98. What does this indicate?
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 generalizes well with no severe overfitting
The model has low error and high R² on both sets, indicating good generalization without significant overfitting. The small difference between training and test RMSE suggests no severe overfitting.
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 has high bias
Why it's wrong here
High bias would result in poor performance on both sets, which is not the case.
- ✓
The model generalizes well with no severe overfitting
Why this is correct
Small difference in RMSE and high test R² indicate good generalization.
- ✗
The model is underfitting because R² is too high
Why it's wrong here
High R² indicates good fit, not underfitting.
- ✗
The model is overfitting because RMSE is lower on training data
Why it's wrong here
A slightly lower training RMSE is normal; the gap is small, so overfitting is not indicated.
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