MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to train a linear regression model. The data scientist notices that the training loss is decreasing but the validation loss has started to increase after a few epochs. What is the most likely cause?
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.
When training loss decreases but validation loss increases, the model is overfitting to the training data. This is a classic sign of overfitting. Underfitting would show both losses high. Learning rate too high would cause divergence. Data leakage would cause both losses to be artificially low.
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 underfitting the training data.
Why it's wrong here
Underfitting would show high training loss, not decreasing.
- ✗
There is data leakage from the validation set into the training set.
Why it's wrong here
Data leakage would cause both losses to be low, not a divergence.
- ✓
The model is overfitting the training data.
Why this is correct
Decreasing training loss with increasing validation loss is a classic sign of overfitting.
- ✗
The learning rate is too high.
Why it's wrong here
A high learning rate would cause the loss to oscillate or diverge, not a smooth decrease then increase.
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