Rborist (version 0.3-7)

validate: Separate Validation of Trained Decision Forest

Description

Permits trained decision forest to be validated separately from training.

Usage

# S3 method for default
validate(train, sampler, preFormat = NULL,  ctgCensus
= "votes", impPermute = 0, quantVec = NULL, quantiles =
!is.null(quantVec), indexing = FALSE, trapUnobserved = FALSE, nThread = 0, verbose =
FALSE, ...)

Value

either of two pairs of objects:

• SummaryReg summarizing regression, as documented with the command predict.arbTrain.

• validation an object of class ValidReg consisting of:

  • mse the mean-square error of the estimate.

  • rsq the r-squared statistic of the estimate.

  • mae the mean absolute error of the estimate.

• SummaryCtg summarizing classification, as documented with the command predict.arbTrain.

• validation an object of class ValidCtg consisting of:

  • confusion the confusion matrix.

  • misprediction the misprediction rate.

  • oobError the out-of-bag error.

Arguments

train

an object of class Rborist obtained from previous training.

sampler

summarizes the response and its per-tree samplgin.

preFormat

internal representation of the design matrix, of class PreFormat

ctgCensus

report categorical validation by vote or by probability.

impPermute

specifies the number of importance permutations: 0 or 1.

quantVec

quantile levels to validate.

quantiles

whether to report quantiles at validation.

indexing

whether to report final index, typically terminal, of tree traversal.

trapUnobserved

indicates whether to return a nonterminal for values unobserved during training, such as missing data.

nThread

suggests an OpenMP-style thread count. Zero denotes the default processor setting.

verbose

indicates whether to output progress of validation.

...

not currently used.

Author

Mark Seligman at Suiji.

Examples

if (FALSE) {
    ## Trains without validation.
    rb <- Rborist(x, y, novalidate=TRUE)
    ...
    ## Delayed validation using a preformatted object.
    pf <- preformat(x)
    v <- validate(pf, rb, y)
  }