SuperLearner.control: Control parameters for the SuperLearner
In SuperLearner: Super Learner Prediction
SuperLearner.control R Documentation
Control parameters for the SuperLearner
Description
Control parameters for the SuperLearner
Usage
SuperLearner.control(saveFitLibrary = TRUE, saveCVFitLibrary = FALSE, trimLogit = 0.001)
Arguments
saveFitLibrary
Logical. Should the fit for each algorithm be saved in the output from SuperLearner.
saveCVFitLibrary
Logical. Should cross-validated fits for each algorithm be saved in the output from SuperLearner.
trimLogit
number between 0.0 and 0.5. What level to truncate the logit transformation to maintain a bounded loss function when using the NNloglik method.
Value
A list containing the control parameters.
SuperLearner documentation built on May 29, 2024, 5:25 a.m.
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| SuperLearner.control | R Documentation |
Control parameters for the SuperLearner
Description
Control parameters for the SuperLearner
Usage
SuperLearner.control(saveFitLibrary = TRUE, saveCVFitLibrary = FALSE, trimLogit = 0.001)
Arguments
saveFitLibrary |
Logical. Should the fit for each algorithm be saved in the output from |
saveCVFitLibrary |
Logical. Should cross-validated fits for each algorithm be saved in the output from |
trimLogit |
number between 0.0 and 0.5. What level to truncate the logit transformation to maintain a bounded loss function when using the NNloglik method. |
Value
A list containing the control parameters.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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