L1 and L2 Loss Function

L1 and L2 are two loss functions in machine learning which are used to minimize the error.

L1 Loss function stands for Least Absolute Deviations. Also known as LAD. $L1LossFunction = \sum_{i=1}^n |y_{true}-y_{predicted}|$

L2 Loss function stands for Least Square Errors. Also known as LS. $L2LossFunction = \sum_{i=1}^n (y_{true}-y_{predicted})^2$

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L1 and L2 are two loss functions in machine learning which are used to minimize the error.

L1 Loss function stands for Least Absolute Deviations. Also known as LAD. $L1LossFunction = \sum_{i=1}^n |y_{true}-y_{predicted}|$

L2 Loss function stands for Least Square Errors. Also known as LS. $L2LossFunction = \sum_{i=1}^n (y_{true}-y_{predicted})^2$