MLS-C01 Modeling Practice Question
A data scientist is training a neural network using a custom loss function. The training process converges, but the model's performance on the validation set is poor. The data scientist suspects that the model is overfitting. Which action should the data scientist take to diagnose overfitting?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that improving training performance (e.g., by adding layers or increasing learning rate) is a valid diagnostic step, when in fact the correct approach is to compare training and validation metrics to detect overfitting.
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
✓
Plot the training and validation loss over epochs
Plotting the training and validation loss over epochs is the standard diagnostic technique for detecting overfitting. If the training loss continues to decrease while the validation loss plateaus or increases, it indicates that the model is memorizing the training data rather than generalizing. This visual comparison directly confirms overfitting, allowing the data scientist to take corrective action such as regularization or early stopping.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Plot the training and validation loss over epochs
Why this is correct
If training loss decreases while validation loss increases, it indicates overfitting.
- ✗
Add more layers to the network
Why it's wrong here
Adding more layers increases model complexity and may worsen overfitting.
- ✗
Increase the learning rate
Why it's wrong here
Increasing the learning rate is a training adjustment, not a diagnostic for overfitting.
- ✗
Compute the confusion matrix on the training set
Why it's wrong here
Confusion matrix shows classification performance but does not directly indicate overfitting.
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