The answer is B. Learning rate.

Here's why:

A. Alpha: This is indeed a hyperparameter in L2 regularization. It determines the amount of shrinkage: the larger the value of alpha, the greater the amount of shrinkage and thus the coefficients become more robust to collinearity.

B. Learning rate: This is not a hyperparameter of L2 regularization, but rather a hyperparameter of the optimization algorithm (like Gradient Descent) used to find the parameters that minimize the loss function.

C. Batch size: This is also not a hyperparameter of L2 regularization. It is a hyperparameter of the optimization algorithm that determines the number of samples to work through before updating the internal model parameters.

D. Epochs: This is not a hyperparameter of L2 regularization. It is a hyperparameter of the optimization algorithm that determines the number times that the learning algorithm will work through the entire training dataset.

Question

The answer is B. Learning rate.

Here's why:

A. Alpha: This is indeed a hyperparameter in L2 regularization. It determines the amount of shrinkage: the larger the value of alpha, the greater the amount of shrinkage and thus the coefficients become more robust to collinearity.

B. Learning rate: This is not a hyperparameter of L2 regularization, but rather a hyperparameter of the optimization algorithm (like Gradient Descent) used to find the parameters that minimize the loss function.

C. Batch size: This is also not a hyperparameter of L2 regularization. It is a hyperparameter of the optimization algorithm that determines the number of samples to work through before updating the internal model parameters.

D. Epochs: This is not a hyperparameter of L2 regularization. It is a hyperparameter of the optimization algorithm that determines the number times that the learning algorithm will work through the entire training dataset.

Knowee AI · Accepted Answer

The answer is B. Learning rate.

Here's why:

A. Alpha: This is indeed a hyperparameter in L2 regularization. It determines the amount of shrinkage: the larger the value of alpha, the greater the amount of shrinkage and thus the coefficients become more robust to collinearity.

B. Learning rate: This is not a hyperparameter of L2 regularization, but rather a hyperparameter of the optimization algorithm (like Gradient Descent) used to find the parameters that minimize the loss function.

C. Batch size: This is also not a hyperparameter of L2 regularization. It is a hyperparameter of the optimization algorithm that determines the number of samples to work through before updating the internal model parameters.

D. Epochs: This is not a hyperparameter of L2 regularization. It is a hyperparameter of the optimization algorithm that determines the number times that the learning algorithm will work through the entire training dataset.

Which of the following is NOT a hyperparameter in L2 regularization?Question 1AnswerA.AlphaB. Learning rateC.Batch sizeD.Epochs

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