2015-06-11T21:30:37 n2552

Bayesian classification and regression trees for predicting incidence of cryptosporidiosis: top 5 of the set of 16 best trees based on sensitivity, specificity, accuracy and deviance

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This dataset was gathered to predict the spatial distribution of the cryptosporidiosis infection using selected social-ecological factors and climate variables. Predictions were completed using a Bayesian CART (Classification and Regression Trees) model.

The dataset presents the top 5 of the set of 16 trees (based on sensitivity, specificity, accuracy and deviance) for Bayesian classification trees.

Location of data collection

kmlPolyCoords
153.552920,-9.929730 137.994575,-9.929730 137.994575,-29.178588 153.552920,-29.178588 153.552920,-9.929730

Publications

Hu, Wen, O'Leary, Rebecca, Mengersen, Kerrie, & Low-Choy, Sama (2011) Bayesian Classification and regression trees for predicting incidence of cryptosporidiosis. PLoS ONE, 6(8), pp. 1-8. http://eprints.qut.edu.au/52459/

Research areas

Cryptosporidium
Crytposporidiosis
Infectious diseases
Spatial distribution
Bayes theorem
Probability distribution
Decision trees
Bayesian

Cite this collection

Hu, Wenbiao; O'Leary, Rebecca A.; Mengersen, Kerrie; Choy, Samantha Low (2013): Top 5 of the set of 16 best trees (based on sensitivity, specificity, accuracy and deviance) for Bayesian classification trees. Table_2.xls. PLOS ONE. 10.1371/journal.pone.0023903.t002

Related information

Rebecca O'Leary, Research collaborator http://goo.gl/LqhdR6

Access the data

Licence


Creative Commons Attribution 4.0 (CC-BY)
http://creativecommons.org/licenses/by/4.0/

Copyright

© 2011 Hu et al.

Dates of data collection

From 2001 to 2011

Connections

Was collected by
Wenbiao Hu  ()

Contacts

Other

Date record created:
2014-07-03T14:01:46
Date record modified:
2015-06-11T21:30:37
Record status:
Published - Open Access