Description Usage Arguments Value Examples
This function approaches the identification of important variables from
rfe
more conservatively than caret. It uses
the standard deviation (or standard error) of the cross-validated error
metric to identify important variables.
1 2 | trainModelVarSelSD(model, metric = model$metric, maximize = FALSE,
sderror = TRUE)
|
model |
a rfe model. See |
metric |
the metric to be used. Note this needs to be the metric used
to calculate the |
maximize |
logical: Is a higher value of the metric favourable (e.g metric = Rsquared) or not (e.g metric = RMSE). maximize=TRUE is determined automatically as long as metric is either Rsquared, ROC, Accuracy. maximize =FALSE is used for all other metrics. Set this manually if you use an other metric where higher values are favourable. |
sderror |
If TRUE then standard error is calculated. If FALSE then standard deviations are used |
a character vector of the variable names
1 | # Not run
|
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