View source: R/rmse_table.SuperLearner.R
rmse_table.SuperLearner | R Documentation |
Calculates root mean-squared error and CIs for each learner in the SuperLearner.
## S3 method for class 'SuperLearner'
rmse_table(x, y = x$Y, sort = TRUE, version = 1, ...)
x |
SuperLearner object |
y |
Outcome vector, if not already added to SL object. |
sort |
Sort table by order of AUC. |
version |
1 (default) or 2. 1 averages the RMSE over folds; 2 averages the MSE and then takes the square root. |
... |
Any additional unused arguments, due to the prauc_table generic. |
Dataframe table with RMSEs.
Faber, N. K. M. (1999). Estimating the uncertainty in estimates of root mean square error of prediction: application to determining the size of an adequate test set in multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 49(1), 79-89.
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
library(SuperLearner)
library(ck37r)
data(Boston, package = "MASS")
set.seed(1)
sl = SuperLearner(Boston$chas, subset(Boston, select = -chas),
family = binomial(),
SL.library = c("SL.mean", "SL.glm"))
rmse_table(sl, y = Boston$chas)
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