Absolute error

As explained in Grokking Machine Learning

A regression error metric equal to the sum of the absolute differences between observed and predicted target values.

Absolute error measures how closely a line fits a dataset. For each point, take the difference between its observed label and predicted label, make that difference positive with an absolute value, then add the distances. A smaller total indicates a better-fitting model. section 3.4

Figure 3.18 The absolute error is the sum of the vertical distances from the points to the line. Note that the absolute error is large for the bad model on the left and small for the good model on the right.

Compare the bad and good fits: the figure shows how their summed vertical distances correspond to larger and smaller absolute errors. section 3.4

How Grokking Machine Learning covers it

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.

How it connects

Connects absolute error with linear regression, error functions, loss functions, cost functions, and square error.

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Also explained in

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  Ensemble Methods for Machine LearningAbsolute error measures prediction error as the absolute difference between a target and its prediction.

Compare how 2 books explain 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.

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.