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Question 3 of 5
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Q3 Single choice

Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...

  • A

    L2 is the sum of the square of the weights, while L1 is just the sum of the weights

  • B

    L1 is the sum of the square of the weights, while L2 is just the sum of the weights

  • C

    L1 gives Non-sparse output while L2 gives sparse outputs

  • D

    None of the above

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