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

Learn the key differences between L1 and L2 loss functions, their uses, and when to prefer each for better model performance.

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L1 loss (mean absolute error) measures the absolute differences between predicted and actual values, emphasizing robustness to outliers. L2 loss (mean squared error) squares the differences, making it sensitive to outliers, but encouraging smaller errors. L1 is often preferred when there are outliers, while L2 is ideal for evenly distributed data with fewer anomalies.

FAQs & Answers

  1. What is L1 loss and how does it work? L1 loss, or mean absolute error, measures the absolute differences between predicted and actual values, making it more robust to outliers.
  2. When should I use L1 loss over L2 loss? L1 loss is preferred in datasets with outliers because it reduces their influence, whereas L2 loss is better for data with normally distributed errors.
  3. Why is L2 loss more sensitive to outliers than L1 loss? L2 loss squares the error terms, which disproportionately increases the impact of larger errors caused by outliers.

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