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SuperLearner (version 2.0-22)

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

Description

summary method for the CV.SuperLearner function

Usage

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

# S3 method for 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 …

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

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.

See Also

CV.SuperLearner