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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.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2019-06-10
Date Created: N/A
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Data Provided by:
uncertainty analysis
Dataset Owner: N/A

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Table representation of structured data
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 N/A
Publisher Name National Institute of Standards and Technology
Contact mailto:david.sheen@nist.gov
Keywords uncertainty analysis , machine learning , model calibration
{
    "identifier": "ark:\/88434\/mds2-2120",
    "accessLevel": "public",
    "references": [
        "https:\/\/doi.org\/10.1007\/s00216-018-1240-2",
        "http:\/\/dx.doi.org\/10.1080\/1062936X.2016.1238010"
    ],
    "contactPoint": {
        "hasEmail": "mailto:david.sheen@nist.gov",
        "fn": "David Sheen"
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    "programCode": [
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    "@type": "dcat:Dataset",
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2120",
    "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": [
        "en"
    ],
    "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"
        }
    ],
    "license": "https:\/\/www.nist.gov\/open\/license",
    "bureauCode": [
        "006:55"
    ],
    "modified": "2019-06-10 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "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"
    ]
}

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