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Discover Ludwig'error measures' is a correct and usable phrase in written English.
Generally, it refers to the methods used to assess the accuracy of a system or model when compared to a set of expected results. For example, "The team used a variety of error measures to test the algorithm's accuracy."
Exact(60)
To assess the quality of the extraction, the typical error measures must be now substituted by approximative measurements.
Different time history durations and different error measures were also considered.
The anisotropic adaptive algorithm requires error measures for keff with directional dependence.
Consistency: Research has shown that consistency and error measures are also important to winning championships.
Hence, it can be used in networks with arbitrary transfer functions and for minimizing a large class of error measures.
The resulting a posteriori error measures involve the solution of both primal and adjoint problems.
We propose specific error measures to assess the success of dejittering.
Finally, multiple error measures have been used to evaluate the quality of the new calibrated model.
A comparison of a number of important a posteriori error measures is made in this work.
The purpose of forecasting error measures is to estimate forecasting methods and choose the best one.
Error measures were used to compare the performance of the M5′ algorithm output to the output from other existing models.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com