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.
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.
Related information
Rebecca O'Leary, Research collaborator
http://goo.gl/LqhdR6
Access the data
Licence
Copyright
© 2011 Hu et al.
Dates of data collection
From 2001 to 2011
Connections
Contacts
Other
Date record created:
2014-07-03T14:01:46
Date record modified:
2019-07-04T11:23:15
Record status:
Published - Open Access