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…

The correct answer is A. L2 is the sum of the square of the weights, while L1 is just the sum of the weights. 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 just that L2 is the sum…

Machine Learning Algorithms

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AL2 is the sum of the square of the weights, while L1 is just the sum of the weights
BL1 is the sum of the square of the weights, while L2 is just the sum of the weights
CL1 gives Non-sparse output while L2 gives sparse outputs
DNone of the above

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A
82% (37)
B
4% (2)
C
11% (5)
D
2% (1)

Explanation

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 just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least

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#L1 regularization#L2 regularization#weight penalty#overfitting

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