MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using SageMaker to train a linear learner algorithm. The training log shows that the algorithm converges but the final loss is still high. Which change is most likely to improve the model?
⚠ Common exam trap
Many candidates confuse convergence (the optimization stopping) with a good model, overlooking that a linear model can converge to a high-loss minimum if the data is non-linear — the fix is feature engineering, not hyperparameter tuning or more data.
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
✓
Add feature crosses or polynomial features
A high final loss despite convergence indicates that the model is underfitting — the linear decision boundary is too simple to capture the underlying patterns in the data. Adding feature crosses or polynomial features increases the model's expressiveness by introducing non-linear interactions, allowing the linear learner to fit more complex relationships and reduce the loss.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the early stopping tolerance
Why it's wrong here
Tighter tolerance may cause early stopping before convergence.
- ✗
Increase the maximum runtime
Why it's wrong here
Runtime does not affect model accuracy.
- ✓
Add feature crosses or polynomial features
Why this is correct
Linear models benefit from feature engineering to capture non-linear relationships.
- ✗
Increase the number of training instances
Why it's wrong here
More instances speed up training but do not improve model performance if underfitting.
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