Dropout is a technique used to prevent overfitting by randomly turning off a fraction of neurons during training.Group of answer choicesTrueFalse

The use of dropout in a neural network can help reduce the risk of overfitting.Group of answer choicesTrueFalse

Dropout prevents a neural network ____________.1 pointfrom overfittingfrom underfittingfrom ideal fit

Dropout increases the model’s training error but decreases the test error, preventing overfitting.Group of answer choicesTrueFalse

What does the dropout regularization technique do during training?Question 3AnswerA.Increases the learning rate dynamically during trainingB.Adds a penalty term to the loss function based on weight magnitudesC.Adds random noise to the input dataD.Sets a fraction of randomly chosen activations to zero

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