What is the Difference Between L1 Loss and L2 Loss in Machine Learning?

Learn the key differences between L1 loss and L2 loss functions, their impact on outliers, and when to use each in ML models.

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L1 loss minimizes the absolute differences between predicted and actual values, making it robust to outliers. L2 loss minimizes the squared differences, penalizing larger errors more, which can make it sensitive to outliers. Use L1 when you need robustness, and L2 when punishing larger errors is essential.

FAQs & Answers

  1. What is L1 loss used for in machine learning? L1 loss is used to minimize the absolute differences between predicted and true values and is especially useful when robustness to outliers is needed.
  2. Why is L2 loss sensitive to outliers? L2 loss squares the differences between predicted and actual values, which heavily penalizes larger errors, making it more sensitive to outliers.
  3. When should I choose L1 loss over L2 loss? Choose L1 loss when you want your model to be robust against outliers or when you need sparse solutions, while L2 loss is preferred when punishing larger errors more severely.

Watch More

For a deeper understanding of how loss functions affect model performance, explore our related videos on Mean Squared Error, Mean Absolute Error, and robust regression techniques.

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