ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.
Resources
2 resources available
-
DOI Access for ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
FILE -
Machine Learning Uncertainty Estimation Toolbox
PYTHON SCRIPTS AND JUPYTER NOTEBOOKS
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "006:55" ] |
| contactPoint |
{ "fn": "David Sheen", "hasEmail": "mailto:david.sheen@nist.gov" } |
| description | This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work. |
| distribution |
[ { "title": "DOI Access for ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models", "accessURL": "https://doi.org/10.18434/M32120" }, { "title": "Machine Learning Uncertainty Estimation Toolbox", "format": "Python scripts and Jupyter notebooks", "accessURL": "https://pages.nist.gov/ml_uncertainty_py/", "description": "This software is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work." } ] |
| identifier | ark:/88434/mds2-2120 |
| issued | 2020-01-21 |
| keyword |
[ "machine learning", "model calibration", "uncertainty analysis" ] |
| landingPage | https://data.nist.gov/od/id/mds2-2120 |
| language |
[ "en" ] |
| license | https://www.nist.gov/open/license |
| modified | 2019-06-10 00:00:00 |
| programCode |
[ "006:045" ] |
| publisher |
{ "name": "National Institute of Standards and Technology", "@type": "org:Organization" } |
| references |
[ "http://dx.doi.org/10.1080/1062936X.2016.1238010", "https://doi.org/10.1007/s00216-018-1240-2" ] |
| theme |
[ "Information Technology:Data and informatics", "Mathematics and Statistics:Numerical methods and software", "Mathematics and Statistics:Uncertainty quantification" ] |
| title | ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models |
