ComputeUncertainty

ComputeUncertainty

is an option for ClassifierMeasurements, LearnedDistribution and other functions to specify if numeric results should be returned along with their uncertainty.

Details

  • Uncertainties are given using Around.
  • The uncertainty interval generated typically corresponds to one standard deviation.

Examples

Basic Examples  (2)

Create and test a classifier using ClassifierMeasurements:

Measure the accuracy along with its uncertainty:

Measure the F1 scores along with their uncertainties:

Train a "Multinormal" distribution on a nominal dataset:

Because of the necessary preprocessing, the PDF computation is not exact:

Use ComputeUncertainty to obtain the uncertainty on the result:

Increase MaxIterations to improve the estimation precision:

Wolfram Research (2019), ComputeUncertainty, Wolfram Language function, https://reference.wolfram.com/language/ref/ComputeUncertainty.html.

Text

Wolfram Research (2019), ComputeUncertainty, Wolfram Language function, https://reference.wolfram.com/language/ref/ComputeUncertainty.html.

BibTeX

@misc{reference.wolfram_2020_computeuncertainty, author="Wolfram Research", title="{ComputeUncertainty}", year="2019", howpublished="\url{https://reference.wolfram.com/language/ref/ComputeUncertainty.html}", note=[Accessed: 16-January-2021 ]}

BibLaTeX

@online{reference.wolfram_2020_computeuncertainty, organization={Wolfram Research}, title={ComputeUncertainty}, year={2019}, url={https://reference.wolfram.com/language/ref/ComputeUncertainty.html}, note=[Accessed: 16-January-2021 ]}

CMS

Wolfram Language. 2019. "ComputeUncertainty." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ComputeUncertainty.html.

APA

Wolfram Language. (2019). ComputeUncertainty. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ComputeUncertainty.html