MLS-C01 Modeling Practice Question
A team is building a product recommendation system using matrix factorization in Amazon SageMaker. They notice that the model's training loss decreases steadily but validation loss starts increasing after 5 epochs. What is the most likely cause?
⚠ Common exam trap
A common mix-up: candidates confuse the symptom of overfitting (training loss decreasing, validation loss increasing) with underfitting or a learning rate issue, but the key is the divergence between the two loss curves after a period of convergence.
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
In matrix factorization for recommendation systems, a decreasing training loss with an increasing validation loss after several epochs is a classic sign of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which degrades its performance on unseen validation data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Underfitting
Why it's wrong here
Underfitting would show high loss on both.
- ✗
Not enough training data
Why it's wrong here
More data typically reduces overfitting.
- ✗
Learning rate too high
Why it's wrong here
High learning rate causes loss to bounce or diverge.
- ✓
Overfitting
Why this is correct
The model is memorizing the training data.
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