MLS-C01 Question 154
Select 3You are training a binary classification model using Amazon SageMaker. During training, you notice that the validation loss stops improving after a few epochs, and the model starts to overfit to the training data. Which of the following strategies could help address the overfitting issue?
- A
Use dropout layers in your model architecture to prevent overfitting.
- B
Reduce the size of your training dataset to make the model generalize better.
- C
Enable early stopping based on the validation loss during training.
- D
Increase the number of epochs for training to allow the model to learn more patterns.
- E
Use L2 regularization (weight decay) to penalize large weights in the model.
- F
Increase the learning rate to make the model converge faster.
Show answer and explanation
Correct answers: A, C, E
Explanation
Overfitting occurs when a model learns patterns specific to the training dataset but fails to generalize to new data. To address this, you can use techniques like dropout layers, early stopping, and L2 regularization, which are designed to improve the model's generalization capability. Reducing the training dataset size, increasing the number of epochs, or raising the learning rate are not appropriate solutions for this scenario.
- A. Correct.
Using dropout layers is a common technique to mitigate overfitting by randomly dropping a fraction of neurons during training, which forces the model to generalize better.
- B. Incorrect.
Reducing the size of your training dataset is not a valid approach to address overfitting. In fact, this could make the issue worse by providing less data for the model to learn from.
- C. Correct.
Enabling early stopping is an effective strategy to prevent overfitting by halting training once the validation loss stops improving.
- D. Incorrect.
Increasing the number of epochs can worsen overfitting as the model may continue learning patterns specific to the training data and fail to generalize.
- E. Correct.
Using L2 regularization (weight decay) is a valid technique to address overfitting as it penalizes large weights, which can help the model generalize better.
- F. Incorrect.
Increasing the learning rate could lead to unstable training and does not directly address overfitting.