2015-06-11T21:30:36 n195

Bayesian classification and regression trees for predicting incidence of cryptosporidiosis: confusion or loss matrix - classification of observed versus predicted presence and absences from the Bayesian CART model

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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 confusion or loss matrix – classification of observed versus predicted presence (‘Yes’) and absences (‘No’) from the Bayesian CART model.

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

Infectious diseases
Classification
Spatial distribution
Cryptosporidium
Decision trees
Bayes theorem
Bayesian
Absences
Observed
Probability distribution
Cryptosporidiosis
Matrix

Cite this collection

Hu, Wenbiao; O'Leary, Rebecca A.; Mengersen, Kerrie; Choy, Samantha Low (2013): Confusion or loss matrix – classification of observed versus predicted presence (‘Yes’) and absences (‘No’) from Bayesian CART model. Table_1.xls. PLOS ONE. 10.1371/journal.pone.0023903.t001

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
Kerrie Mengersen  (Researcher)
Sama Low Choy  (Researcher)
Wenbiao Hu  (Researcher)

Contacts

Other

Date record created:
2014-07-02T15:01:58
Date record modified:
2015-06-11T21:30:36
Record status:
Published - Open Access