Data provided by National Institute of Standards and Technology
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.
About this Dataset
Updated: 2024-02-22
Metadata Last Updated:
2019-06-10 00:00:00
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uncertainty analysis
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| Title | ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models |
|---|---|
| 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. |
| Modified | 2019-06-10 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:david.sheen@nist.gov |
| Keywords | uncertainty analysis , machine learning , model calibration |
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"accessLevel": "public",
"references": [
"https:\/\/doi.org\/10.1007\/s00216-018-1240-2",
"http:\/\/dx.doi.org\/10.1080\/1062936X.2016.1238010"
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"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.",
"language": [
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"title": "ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models",
"distribution": [
{
"accessURL": "https:\/\/pages.nist.gov\/ml_uncertainty_py\/",
"format": "Python scripts and Jupyter notebooks",
"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.",
"title": "Machine Learning Uncertainty Estimation Toolbox"
},
{
"accessURL": "https:\/\/doi.org\/10.18434\/M32120",
"title": "DOI Access for ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models"
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"license": "https:\/\/www.nist.gov\/open\/license",
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"modified": "2019-06-10 00:00:00",
"publisher": {
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"name": "National Institute of Standards and Technology"
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"theme": [
"Mathematics and Statistics:Uncertainty quantification",
"Mathematics and Statistics:Numerical methods and software",
"Information Technology:Data and informatics"
],
"issued": "2020-01-21",
"keyword": [
"uncertainty analysis",
"machine learning",
"model calibration"
]
}
