summary.CV.SuperLearner: Summary Function for Cross-Validated Super Learner

In ecpolley/SuperLearner: Super Learner Prediction

Summary Function for Cross-Validated Super Learner

Description

summary method for the CV.SuperLearner function

Usage

## S3 method for class 'CV.SuperLearner'
summary(object, obsWeights = NULL, ...)

## S3 method for class 'summary.CV.SuperLearner'
print(x, digits, ...)

Arguments

object	An object of class "CV.SuperLearner", the result of a call to CV.SuperLearner.
x	An object of class "summary.CV.SuperLearner", the result of a call to summary.CV.SuperLearner.
obsWeights	Optional vector for observation weights.
digits	The number of significant digits to use when printing.
...	additional arguments ...

Details

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.

Value

summary.CV.SuperLearner returns a list with components

call	The function call from CV.SuperLearner
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

Author(s)

Eric C Polley [e-mail]

See Also

CV.SuperLearner

ecpolley/SuperLearner documentation built on Feb. 21, 2024, 11:38 p.m.