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Reference Guide for OpenMatrix Language Functions
The Reference Guide contains documentation for all functions supported in the OpenMatrix language.
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maxerror
Computes Maximum Absolute Deviation between two vectors. It is the maximum of the error made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

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- Extended Definitions for Advanced Users
  PDF file with in-depth information on key topics in the User's Guide.
- Scripting Guide for OpenMatrix Language
  OpenMatrix is a mathematical scripting language.
- Reference Guide for OpenMatrix Language Functions
  The Reference Guide contains documentation for all functions supported in the OpenMatrix language.
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        Computes Half Mean Squared Error between two vectors. It is half of the average error made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).
      - mae
        Computes Mean Absolute Error between two vectors. It is the average of the error (absolute error) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).
      - maxerror
        Computes Maximum Absolute Deviation between two vectors. It is the maximum of the error made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).
      - mse
        Computes Mean Squared Error between two vectors. It is the average of the error (squared to get rid of negative sign) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).
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        Computes the Coefficient of Determination, which is the proportion of variance in the dependent variable that is predictable from the independent variable(s), where the best value is 1 and the worst value goes towards -Infinity.
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        Computes Root Mean Squared Error between two vectors. It is the average of the amount of mistakes made by regression model in prediction, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).
      - rss
        Computes Residual Sum of Squares between two vectors. It is the sum of the error (squared to get rid of negative sign) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).
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Frequently Asked Questions
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maxerror

Computes Maximum Absolute Deviation between two vectors. It is the maximum of the error made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

Attention: Available only with Twin Activate commercial edition.

Syntax

Score = maxerror(targets,predictions)

Inputs

targets: Actual label for each observation.; Type: double; Dimension: vector
predictions: Predicted value for each observation.; Type: double; Dimension: vector

Outputs

Score: Maximum absolute deviation of the regression model.; Type: double; Dimension: scalar

Example

Usage of maxerror

targets = [3.14, 0.1, 50, -2.5];
predictions = [3.1, 0.5, 50.3, -5];
score = maxerror(targets, predictions);

> score
score = 2.5