{
    "identifier": "ark:\/88434\/mds2-2227",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Peter Fontana"
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/github.com\/usnistgov\/active-evaluation",
    "title": "Active Evaluation Software for Selection of Ground Truth Labels",
    "description": "This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "accessURL": "https:\/\/doi.org\/10.18434\/M32227",
            "title": "DOI Access for Active Evaluation Software for Selection of Ground Truth Labels"
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2020-04-28 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Information Technology:Data and informatics"
    ],
    "keyword": [
        "active evaluation",
        "machine learning",
        "ar"
    ]
}