View source: R/prauc_table.SuperLearner.R
prauc_table.SuperLearner | R Documentation |
Calculates precision-recall AUC for each learner in the SuperLearner.
## S3 method for class 'SuperLearner'
prauc_table(x, y = x$Y, sort = TRUE, ...)
x |
SuperLearner object |
y |
Outcome vector, if not already added to SL object. |
sort |
Sort table by order of AUC. |
... |
Any additional unused arguments, due to the prauc_table generic. |
Dataframe table with PR-AUCs.
Boyd, K., Eng, K. H., & Page, C. D. (2013). Area under the precision-recall curve: point estimates and confidence intervals. In Joint European conference on machine learning and knowledge discovery in databases (pp. 451-466). Springer, Berlin, Heidelberg.
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
library(SuperLearner)
library(ck37r)
data(Boston, package = "MASS")
set.seed(1)
sl = SuperLearner(Boston$chas, subset(Boston, select = -chas),
family = binomial(),
SL.library = c("SL.mean", "SL.glm"))
prauc_table(sl, y = Boston$chas)
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