Which of the following is NOT a hyperparameter in L2 regularization?Question 1AnswerA.AlphaB. Learning rateC.Batch sizeD.Epochs
Question
Which of the following is NOT a hyperparameter in L2 regularization?
Question 1
Answer
A. Alpha
B. Learning rate
C. Batch size
D. Epochs
Solution
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.
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