learn {ofw}R Documentation

Learning ofw for error rate assessement

Description

learn simply performs the learning step of ofw on several bootstrap samples in order to assess the error rate on the test observations (see evaluate)

Usage

## Default S3 method:
learn(x, y, type="CART", ntree= if(type=="CART") 50 else NULL, 
        nforest= if(type=="CART") 100 else NULL, nsvm= if(type=="SVM") 
        20000 else NULL, mtry=5, do.trace=FALSE, nstable=50, weight=FALSE, 
        Bsample=5, ...)

Arguments

x A data frame with continuous values.
y A response vector given as a factor (classification only).
type Classifier used: either CART or SVM.
ntree If CART, number of trees to grow for each iteration (trees aggregation).
nforest If CART, number of iterations to run. This should not be set to too small a number, to ensure the convergence of the algorithm.
nsvm If SVM, number of iterations to run. This should be set to a very large number, to ensure the convergence of the algorithm.
mtry Number of variables sampled according to the weight vector P as candidates for each tree or SVM. This should be small enough to ensure stable results of the algorithm.
do.trace If set to some integer, then current iteration is printed for every do.trace iterations and the number of the first stable variables is output.
nstable Need do.trace set to some integer. Stopping criterion before nforest iterations are reached: if the nstable first weighted variables are the same after do.stable iterations, then stop.
weight Should the weighting procedure be applied ?
Bsample Number of bootstrap samples for the learning step.
... not used currently.

Details

The object from class learn will be used in the generic function evaluate

Value

An object of class learn, which is a list with the following components:

x Original input
y Original predictor
type Classifier used: either CART or SVM
nsample Total number of samples
nclass Number of levels of y (number of classes)
weight If TRUE the weighted procedure was performed during the learning.
Bsample Number of bootstrap samples on which ofwCART is learnt.
matTrain A n by Bsample matrix indicating the training samples in each bootstrap sample.
matProb A nvariable by Bsample matrix for each probability distribution learnt.
classWeight If weight = TRUE class weight vector.
sampleWeight If weight = TRUE sample weight vector.

Note

The computation of learn is slow as it requires to launch ofw Bsample times.

Parallelized computations are possible with ofw and Rmpi library for the learning step and evaluateCARTparallel for the evaluation step.

Author(s)

Kim-Anh L^e Cao Kim-Anh.Le-Cao@toulouse.inra.fr

Patrick Chabrier Patrick.Chabrier@toulouse.inra.fr

References

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. L^e Cao, K-A., Bonnet, A., and Gadat, S., Multiclass classification and gene selection with a stochastic algorithm http://www.lsp.ups-tlse.fr/Recherche/Publications/2007/cao05.html.

See Also

evaluate.learn

Examples

## On data set "data"
#data(srbct)
#attach(srbct)
#learn.boot.cart <- learnCART(srbct, as.factor(class),type="CART", ntree=50, nforest=200, mtry=5, Bsample=3)
#learn.boot.svm <- learnSVM(srbct, as.factor(class), type="SVM", nsvm=500, mtry=5, Bsample=3)
#detach(srbct)

[Package ofw version 1.0-0 Index]