an expression of the formlist(name = expression, ...)representing the first round of variable transformations. As with all expressions,transforms(orrowSelection) can be defined outside of the function call using the expression function. transformObjects ...
optional list with components specifying additional parameters for the "class" splitting method, as follows: prior - a vector of prior probabilities. The priors must be positive and sum to 1. The default priors are proportional to the data counts. loss - a loss matrix, which must have zeros...
an object of class "rxDForest". It is a list with the following components, similar to those of class "randomForest":ntreeThe number of trees.mtryThe number of variables tried at each split.typeOne of "class" (for classification) or "anova" (for regression)....
optional list with components specifying additional parameters for the "class" splitting method, as follows: prior - a vector of prior probabilities. The priors must be positive and sum to 1. The default priors are proportional to the data counts. loss - a loss matrix, which must have zeros...
Utility Functions for rxDForest.Usage複製 rxVarImpPlot(x, sort = TRUE, n.var = 30, main = deparse(substitute(x)), ... ) rxLeafSize(x, use.weight = TRUE) rxTreeDepth(x) rxTreeSize(x, terminal = TRUE) rxVarUsed(x, by.tree = FALSE, count = TRUE) rxGetTree(x, k = 1) ...
an expression of the formlist(name = expression, ...)representing the first round of variable transformations. As with all expressions,transforms(orrowSelection) can be defined outside of the function call using the expression function. transformObjects ...
an object of class "rxDForest". It is a list with the following components, similar to those of class "randomForest":ntreeThe number of trees.mtryThe number of variables tried at each split.typeOne of "class" (for classification) or "anova" (for regression)....