Absolute error
Absolute error is an error measure based on the nonnegative absolute difference between an observed value and its prediction, optionally aggregated across observations.
Grokking Machine Learning
A regression error metric equal to the sum of the absolute differences between observed and predicted target values.
- How it is used
- Measures how closely a linear-regression line fits dataset points by summing their vertical distances.
- How deep it goes
- Explains how absolute values handle positive or negative label differences before summing distances.
- Examples
- Compares bad and good fitted lines by their larger and smaller absolute errors.
- What you can do after
- Evaluate a linear-regression fit by adding the absolute differences between observed and predicted labels.
Read how Grokking Machine Learning explains absolute error →
Ensemble Methods for Machine Learning
Absolute error measures prediction error as the absolute difference between a target and its prediction.
- How it is used
- Measures continuous-valued regression error while limiting the influence of outliers.
- How deep it goes
- Defines the formula, likelihood interpretation, outlier behavior, and differentiability limit for Newton boosting.
- Examples
- Visualizes absolute error alongside squared error and other loss functions in Figure 7.10.
- What you can do after
- Evaluate regression loss choices by comparing outlier sensitivity, likelihood assumptions, and differentiability.
Read how Ensemble Methods for Machine Learning explains absolute error →
Related concepts
- squared losscontrasts with · Squared loss is the square of the difference between a predicted or fitted value and its target or observation, often summed across data points.
- objective functionkind of · An objective function assigns values to candidate solutions, predictions, or parameter settings according to a criterion to be minimized or maximized.
- mean absolute errorproduces · Mean absolute error is the arithmetic mean of the absolute differences between corresponding predicted and reference numerical values.
- distancehas part · Distance is a measure of separation between points or entities in a defined space or context.
- forecast errorproduced by · A forecast error is the difference between an observed value and the corresponding forecasted value.
- cross entropy lossalternative to · Cross-entropy loss measures the expected negative log probability that a predicted distribution assigns to outcomes drawn from a target distribution.
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