evaluateCARTparallel {ofw} | R Documentation |
The error rate assessment e.632+ (Efron and Tibshirani, 1997) is performed on ofwCART. This second version of evaluateCART
allows to perform the learning step independently (for example with parallel computing).
evaluateCARTparallel(x, y, matTrain, matProb, maxvar=15, nvar=nlevels(y)+1, ntreeTest=100, weight=FALSE)
x |
A data frame with continuous values. |
y |
A response vector given as a factor (classification only). |
matTrain |
A nsample by Bsample matrix indicating the training samples in each bootstrap sample. |
matProb |
A nvariable by Bsample matrix for each probability distribution learnt. |
maxvar |
Size of the evaluated variable selection. |
nvar |
Number of randomly sampled variables in the selection that are used to construct each tree. Should be at least nlevels(y)+1 to ensure generalizable trees. |
ntreeTest |
Number of trees aggregated to evaluate the performance of ofwCART when each variable enters the selection. |
weight |
Should the weighting procedure be applied during the evaluation phase ? |
see evaluateCART
An object of class evaluateCARTparallel
, which is a list with the
following components:
maxvar |
Size of the evaluated variable selection. |
nvar |
Number of randomly sampled variables in the selection that are used to construct each tree. |
weight.eval |
Was the weighting procedure applied during the evaluation step ? |
ntreeTest |
Number of aggregated trees as each variable enters the selection. |
matTrain |
A nsample by Bsample matrix indicating the training samples in each bootstrap sample. |
matProb |
A nvariable by Bsample matrix for each probability distribution learnt. |
error |
The evaluated e.632+ boostrap error as each variable enters the selection. |
sampleWeight |
if weight = TRUE , a n by Bsample matrix indicating each sample weight in each bootstrap sample. |
matPredInbag |
A nvariable by Bsample matrix indicating the prediction of the inbag samples. |
matPredTest |
A nvariable by Bsample matrix indicating the prediction of the test samples. |
The e.632+ code comes from the ipred
package from Thorsten.
This type of evaluation should only be used to compare several methods and not to assess the performance of only one method.
Kim-Anh L^e Cao Kim-Anh.Le-Cao@toulouse.inra.fr
Patrick Chabrier Patrick.Chabrier@toulouse.inra.fr.
Efron, B. and Tibshirani R.J. (1997), Improvements on cross-validation: the e.632+ bootstrap method, Journal of American Statistical Association 92, 548-560.
L^e Cao, K-A., Gonc calves, O., Besse, P. and Gadat, S. (2007), Selection of biologically relevant genes with a wrapper stochastic algorithm Statistical Applications in Genetics and Molecular Biology: Vol. 6: Iss.1, Article 29.
evaluate
## On data set "data" ##first learn ofwCART on Bsample bootstrap samples and store matTrain.data and matProb.data ##data.evalCARTparallel <- evaluateCARTparalell(data[,-1], as.factor(data[,1], matTrain=matTrain.data, matProb=matProb.data, ntreeTest=50, maxvar=10) ##plot(data.evalCARTparallel$error, type="l")