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=∑i=1n∣ytrue−ypredicted∣L1LossFunction = \sum_{i=1}^n |y_{true}-y_{predicted}|L1LossFunction=∑i=1n∣ytrue−ypredicted∣
L2 Loss function stands for Least Square Errors. Also known as LS. L2LossFunction=∑i=1n(ytrue−ypredicted)2L2LossFunction = \sum_{i=1}^n (y_{true}-y_{predicted})^2L2LossFunction=∑i=1n(ytrue−ypredicted)2
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