MLA-C01 Question 182
Select 3You are training a deep neural network on a dataset with significant overfitting issues. Which of the following regularization techniques could help reduce overfitting?
- A
Apply dropout to randomly deactivate neurons during training.
- B
Use L1 regularization to encourage sparsity in the model parameters.
- C
Increase the model's learning rate to make training faster.
- D
Apply L2 regularization to penalize large weights in the model.
- E
Remove validation data to provide more training samples.
Show answer and explanation
Correct answers: A, B, D
Explanation
Regularization techniques like dropout, L1, and L2 regularization are effective in reducing overfitting by constraining the model's capacity to memorize training data. Dropout prevents co-adaptation of neurons, L1 encourages sparsity, and L2 reduces large weight magnitudes. These methods force the model to generalize better, leading to improved performance on unseen data.
- A. Correct.
Correct: Dropout is a regularization method that reduces overfitting by randomly deactivating neurons during training, forcing the network to learn more robust features.
- B. Correct.
Correct: L1 regularization penalizes the absolute values of weights, encouraging sparsity in the model, which can help prevent overfitting.
- C. Incorrect.
Incorrect: Increasing the model's learning rate can lead to unstable convergence and does not directly address overfitting.
- D. Correct.
Correct: L2 regularization penalizes large weights by adding the squared magnitude of weights to the loss function, helping to prevent overfitting.
- E. Incorrect.
Incorrect: Removing validation data does not address overfitting and can lead to poor generalization since validation data is necessary to monitor the model's performance on unseen data.