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.
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.
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:
2014-07-02T15:01:58
Date record modified:
2019-07-04T11:18:16
Record status:
Published - Open Access