Found 8 datasets matching "eXtreme Gradient Boosting".
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A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and geospatial data to predicted...
Search relevance: 117.87 | Views last month: 0 -
This child item dataset includes inputs to and outputs from an extreme gradient boosting model (XGBoost) to predict daily chlorophyll concentrations. Included in this dataset are the necessary...
Search relevance: 94.52 | Views last month: 0 -
This data release contains data used to develop models and maps that estimate the occurrence of lithium in groundwater used as drinking water throughout the conterminous United States. An extreme...
Search relevance: 67.29 | Views last month: 9 -
This data release contains modeled daily chlorophyll concentration for 82 streams and rivers across the conterminous United States. Estimates of daily chlorophyll concentration were generated...
Search relevance: 66.78 | Views last month: 0 -
An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was...
Search relevance: 61.85 | Views last month: 0 -
A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in groundwater across the conterminous United States (CONUS)....
Search relevance: 59.47 | Views last month: 0 -
An extreme gradient boosting ensemble tree model predicting per- and polyfluoroalkyl substances (PFAS) occurrence in groundwater at the depths typical of the bottom of public and domestic drinking...
Search relevance: 52.76 | Views last month: 5 -
The data contained herein are five input features (i.e., heat flow, distance to the nearest quaternary fault, distance to the nearest quaternary magma body, seismic event density, maximum...
Search relevance: 49.16 | Views last month: 0
