View source: R/summary.CV.SuperLearner.R
summary.CV.SuperLearner | R Documentation |
summary method for the CV.SuperLearner
function
## S3 method for class 'CV.SuperLearner'
summary(object, obsWeights = NULL, ...)
## S3 method for class 'summary.CV.SuperLearner'
print(x, digits, ...)
object |
An object of class "CV.SuperLearner", the result of a call to |
x |
An object of class "summary.CV.SuperLearner", the result of a call to |
obsWeights |
Optional vector for observation weights. |
digits |
The number of significant digits to use when printing. |
... |
additional arguments ... |
Summary method for CV.SuperLearner
. Calculates the V-fold cross-validated estimate of either the mean squared error or the -2*log(L) depending on the loss function used.
summary.CV.SuperLearner
returns a list with components
call |
The function call from |
method |
Describes the loss function used. Currently either least squares of negative log Likelihood. |
V |
Number of folds |
Risk.SL |
Risk estimate for the super learner |
Risk.dSL |
Risk estimate for the discrete super learner (the cross-validation selector) |
Risk.library |
A matrix with the risk estimates for each algorithm in the library |
Table |
A table with the mean risk estimate and standard deviation across the folds for the super learner and all algorithms in the library |
Eric C Polley eric.polley@nih.gov
CV.SuperLearner
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