Which of the following techniques performs similar operations as a dropout in a neural network?Question 5Select one:A.StackingB.NoneC.BoostingD.Bagging

Breakdown of the Problem

1. Identify the nature of the operations performed by dropout in neural networks.
2. Analyze the options provided to find similarities with dropout.

Relevant Concepts

• Dropout: This is a regularization technique used in neural networks to prevent overfitting by randomly setting a fraction of the input units to zero at each update during training time.
• Stacking: Combining multiple models to improve predictive performance.
• Boosting: An ensemble technique that adjusts the weights of instances based on the errors of previous models.
• Bagging: An ensemble method that improves the stability and accuracy of machine learning algorithms by combining the predictions from multiple models trained on different subsets of the data.

Analysis and Detail

1. Dropout: Reduces overfitting by deactivating neurons randomly, which forces the network to learn more robust features.
2. Boosting and Bagging: Both involve creating ensembles of models but do not deactivate units per training iteration like dropout.
3. Stacking: This involves combining different models, but again, does not share the mechanism of randomly dropping units.

Verify and Summarize

After analyzing the function of dropout and comparing it with the provided options, none of them perform the same random deactivation operation that dropout does.

Final Answer

B. None is the correct answer, as none of the listed techniques perform similar operations to dropout in neural networks.