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

ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://goo.gl/Izocug&rft.title=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&rft.identifier=10378.3/8085/1018.15730&rft.publisher=Queensland University of Technology&rft.description=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.&rft.creator=Adjunct Associate Professor Sama Low Choy&rft.creator=Distinguished Professor Kerrie Mengersen&rft.creator=Professor Wenbiao Hu&rft.date=2015&rft.coverage=153.552920,-9.929730 137.994575,-9.929730 137.994575,-29.178588 153.552920,-29.178588 153.552920,-9.929730&rft_rights=© 2011 Hu et al.&rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/au/&rft_subject=Bayesian&rft_subject=Decision trees&rft_subject=Probability distribution&rft_subject=Bayes theorem&rft_subject=Spatial distribution&rft_subject=Infectious diseases&rft_subject=Crytposporidiosis&rft_subject=Cryptosporidium&rft.type=dataset&rft.language=English Access the data