randomForestInfJackMulticlass: The infinitesimal jackknife for random forests (multiclass...
In swager/randomForestCI: Confidence Intervals for Random Forests
Description
The infinitesimal jackknife for random forests (multiclass target variable)
Usage
1
randomForestInfJackMulticlass(rf, newdata, calibrate = TRUE)
Arguments
rf
A random forest trained with replace = TRUE and keep.inbag = TRUE
newdata
A set of test points at which to evaluate standard errors
calibrate
whether to apply calibration to mitigate Monte Carlo noise
warning: if calibrate = FALSE, some variance estimates may be negative
due to Monte Carlo effects if the number of trees in rf is too small
swager/randomForestCI documentation built on May 30, 2019, 9:33 p.m.
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Description
The infinitesimal jackknife for random forests (multiclass target variable)
Usage
1 | randomForestInfJackMulticlass(rf, newdata, calibrate = TRUE)
|
Arguments
rf |
A random forest trained with replace = TRUE and keep.inbag = TRUE |
newdata |
A set of test points at which to evaluate standard errors |
calibrate |
whether to apply calibration to mitigate Monte Carlo noise warning: if calibrate = FALSE, some variance estimates may be negative due to Monte Carlo effects if the number of trees in rf is too small |
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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