Data to support Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies
This data release contains one dataset and one model archive in support of the journal article "Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies" by Jennifer C. Murphy and Jeffrey G. Chanat. The model archive contains scripts (run in R) to reproduce the four machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) trained and tested as part of the journal article. The dataset contains the estimated probabilities for each of these models when applied to a training and test dataset.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Jennifer C Murphy",
"@type": "vcard:Contact",
"hasEmail": "mailto:jmurphy@usgs.gov"
}
|
| description | This data release contains one dataset and one model archive in support of the journal article "Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies" by Jennifer C. Murphy and Jeffrey G. Chanat. The model archive contains scripts (run in R) to reproduce the four machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) trained and tested as part of the journal article. The dataset contains the estimated probabilities for each of these models when applied to a training and test dataset. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P9GNEN8S",
"mediaType": "application/http",
"description": "Landing page for access to the data"
},
{
"@type": "dcat:Distribution",
"title": "Original Metadata",
"format": "XML",
"mediaType": "text/xml",
"description": "The metadata original format",
"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.647a3349d34eac007b521f2d.xml"
}
]
|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_647a3349d34eac007b521f2d |
| keyword |
[
"Delaware River Basin",
"USGS:647a3349d34eac007b521f2d",
"United States of America",
"biota",
"k-nearest neighbors",
"linear discriminant analysis",
"logistic regression",
"quadratic discriminant analysis"
]
|
| modified | 2023-10-04T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -127.7930, 24.0465, -64.6875, 49.8380 |
| theme |
[
"geospatial"
]
|
| title | Data to support Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies |
