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MLS-C01 Modeling Practice Question

A data scientist trains a convolutional neural network (CNN) for image classification. The training loss decreases steadily, but the validation loss starts increasing after 10 epochs. Which technique should the data scientist use to address this problem?

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

Use early stopping to halt training when validation loss stops decreasing.

Early stopping halts training when validation loss stops improving, preventing overfitting. Option A (increasing data augmentation) may help reduce overfitting but does not directly address the already occurring validation loss increase. Option C (more epochs) would worsen overfitting. Option D (more convolutional layers) increases model capacity, likely worsening overfitting. Option E (higher learning rate) may cause divergence or instability.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Add more data augmentation to the training set.

    Why it's wrong here

    Data augmentation helps generalization but is not the immediate fix for overfitting already occurring.

  • Use early stopping to halt training when validation loss stops decreasing.

    Why this is correct

    Early stopping prevents overfitting by stopping at the optimal point.

  • Increase the number of training epochs.

    Why it's wrong here

    More epochs will likely increase overfitting.

  • Add more convolutional layers to increase model capacity.

    Why it's wrong here

    More capacity can worsen overfitting.

  • Increase the learning rate.

    Why it's wrong here

    Higher learning rate may cause training instability.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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