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MLA-C01 Practice Question: Training a binary classifier in SageMaker and…
A company is training a binary classifier in SageMaker and observes that the training loss decreases but validation loss increases after a few epochs. What is the most likely issue?
⚠ Common exam trap
Candidates often mistake the pattern of decreasing training loss with increasing validation loss for a high learning rate, but a high learning rate would typically cause both losses to oscillate or diverge, not show this specific pattern.
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
The training loss decreasing while validation loss increasing after a few epochs is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which causes it to perform poorly on unseen validation data. In SageMaker, this often occurs when the model has too many parameters relative to the dataset size, or when regularization techniques like dropout or L2 weight decay are insufficient.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Learning rate too high
Why it's wrong here
A high learning rate makes the loss oscillate or diverge, so both training and validation loss would rise or fluctuate, not split apart. It is tempting because an excessive rate genuinely destabilises training, and lowering it is the right fix when loss explodes or fails to decrease at all.
- ✓
Overfitting
Why this is correct
Diverging loss curves — training loss falling while validation loss rises — is the defining signature of overfitting, where the model memorises training noise rather than generalising. The gap widens after several epochs, matching the stem's observation precisely.
- ✗
Underfitting
Why it's wrong here
Underfitting shows both training and validation loss remaining high and flat, not diverging. It would be the diagnosis when a model is too simple to capture the pattern, which is the opposite of the described widening gap.
- ✗
Data imbalance
Why it's wrong here
Class imbalance skews predictions toward the majority label and depresses minority-class recall, but it does not by itself produce training loss falling while validation loss rises. Imbalance calls for resampling or class weights, not early stopping.
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