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MLS-C01 Modeling Practice Question

A data scientist is training a recurrent neural network (RNN) for time series forecasting. The training loss decreases steadily for the first 10 epochs, then plateaus. The validation loss starts increasing after epoch 10. What is the most appropriate action?

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

Stop training early and use the model from epoch 10

The validation loss increasing while training loss decreases indicates overfitting. Early stopping halts training before overfitting worsens, preserving the model that performed best on validation data (epoch 10). Option B (continuing training) would increase overfitting. Option C (adding layers) may exacerbate overfitting. Option D (increasing batch size) is not directly addressing overfitting and may not help.

Answer analysis

Option-by-option breakdown

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

  • Stop training early and use the model from epoch 10

    Why this is correct

    Early stopping prevents overfitting; the model at epoch 10 generalizes better.

  • Continue training for more epochs

    Why it's wrong here

    Validation loss is increasing, so more epochs will worsen overfitting.

  • Add more layers to the network

    Why it's wrong here

    More layers increase capacity and may worsen overfitting.

  • Increase the batch size

    Why it's wrong here

    Batch size affects convergence but not overfitting directly.

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