{"@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": [{"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.", "format": "Python scripts and Jupyter notebooks", "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"}], "identifier": "ark:/88434/mds2-2120", "issued": "2020-01-21", "keyword": ["uncertainty analysis", "machine learning", "model calibration"], "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": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "references": ["https://doi.org/10.1007/s00216-018-1240-2", "http://dx.doi.org/10.1080/1062936X.2016.1238010"], "theme": ["Mathematics and Statistics:Uncertainty quantification", "Mathematics and Statistics:Numerical methods and software", "Information Technology:Data and informatics"], "title": "ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models"}