L1 loss: Revision history

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18 March 2023

  • curprev 13:1113:11, 18 March 2023‎ Walle talk contribs‎ 3,112 bytes +3,112‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, various loss functions are used to measure the discrepancy between predicted values and actual values. L1 loss, also known as ''Least Absolute Deviations'' (LAD) or ''Least Absolute Errors'' (LAE), is one such loss function used in regression problems to estimate model parameters. L1 loss calculates the sum of absolute differences between predicted and actual values, making it robust to outliers an..."
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