GAMens.cv: Runs v-fold cross validation with GAMbag, GAMrsm or GAMens ensemble classifier

Description

In v-fold cross validation, the data are divided into v subsets of approximately equal size. Subsequently, one of the v data parts is excluded while the remainder of the data is used to create a GAMens object. Predictions are generated for the excluded data part. The process is repeated v times.

Usage

GAMens.cv(formula, data, cv, rsm_size = 2, autoform = FALSE, iter = 10,
  df = 4, bagging = TRUE, rsm = TRUE, fusion = "avgagg")

Arguments

formula

a formula, as in the gam function. Smoothing splines are supported as nonparametric smoothing terms, and should be indicated by s. See the documentation of s in the gam package for its arguments. The GAMens function also provides the possibility for automatic formula specification. See 'details' for more information.

data

a data frame in which to interpret the variables named in formula.

An integer specifying the number of folds in the cross-validation.

rsm_size

an integer, the number of variables to use for random feature subsets used in the Random Subspace Method. Default is 2. If rsm=FALSE, the value of rsm_size is ignored.

autoform

if FALSE (by default), the model specification in formula is used. If TRUE, the function triggers automatic formula specification. See 'details' for more information.

iter

an integer, the number of base (member) classifiers (GAMs) in the ensemble. Defaults to iter=10 base classifiers.

an integer, the number of degrees of freedom (df) used for smoothing spline estimation. Its value is only used when autoform = TRUE. Defaults to df=4. Its value is ignored if a formula is specified and autoform is FALSE.

bagging

enables Bagging if value is TRUE (default). If FALSE, Bagging is disabled. Either bagging, rsm or both should be TRUE

rsm

enables Random Subspace Method (RSM) if value is TRUE (default). If FALSE, rsm is disabled. Either bagging, rsm or both should be TRUE

fusion

specifies the fusion rule for the aggregation of member classifier outputs in the ensemble. Possible values are 'avgagg' for average aggregation (default), 'majvote' for majority voting, 'w.avgagg' for weighted average aggregation based on base classifier error rates, or 'w.majvote' for weighted majority voting.

Value

An object of class GAMens.cv, which is a list with the following components:

foldpred

a data frame with, per fold, predicted class membership probabilities for the left-out observations.

pred

a data frame with predicted class membership probabilities.

foldclass

a data frame with, per fold, predicted classes for the left-out observations.

class

a data frame with predicted classes.

conf

the confusion matrix which compares the real versus predicted class memberships, based on the class object.

References

De Bock, K.W. and Van den Poel, D. (2012): "Reconciling Performance and Interpretability in Customer Churn Prediction Modeling Using Ensemble Learning Based on Generalized Additive Models". Expert Systems With Applications, Vol 39, 8, pp. 6816--6826.

De Bock, K. W., Coussement, K. and Van den Poel, D. (2010): "Ensemble Classification based on generalized additive models". Computational Statistics & Data Analysis, Vol 54, 6, pp. 1535--1546.

Breiman, L. (1996): "Bagging predictors". Machine Learning, Vol 24, 2, pp. 123--140.

Hastie, T. and Tibshirani, R. (1990): "Generalized Additive Models", Chapman and Hall, London.

Ho, T. K. (1998): "The random subspace method for constructing decision forests". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 20, 8, pp. 832--844.

Examples

Run this code

# NOT RUN {
## Load data: mlbench library should be loaded!)
library(mlbench)
data(Sonar)
SonarSub<-Sonar[,c("V1","V2","V3","V4","V5","V6","Class")]

## Obtain cross-validated classification performance of GAMrsm
## ensembles, using all variables in the Sonar dataset, based on 5-fold
## cross validation runs

Sonar.cv.GAMrsm <- GAMens.cv(Class~s(V1,4)+s(V2,3)+s(V3,4)+V4+V5+V6,
SonarSub ,5, 4 , autoform=FALSE, iter=10, bagging=FALSE, rsm=TRUE )

## Calculate AUCs (for function colAUC, load caTools library)
library(caTools)

GAMrsm.cv.auc <- colAUC(Sonar.cv.GAMrsm[[2]], SonarSub["Class"]=="R",
plotROC=FALSE)


# }