predict.GAMens {GAMens}

Predicts from a fitted GAMens object (i.e., GAMbag, GAMrsm or GAMens classifier).

Description

Generates predictions (classes and class membership probabilities) for observations in a dataframe using a GAMens object (i.e., GAMens, GAMrsm or GAMbag classifier).

Usage

## S3 method for class 'GAMens'
predict(object, data, ...)

Arguments

object

fitted model object of GAMens class.

data

data frame with observations to genenerate predictions for.

...

further arguments passed to or from other methods.

Value

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

pred

the class membership probabilities generated by the ensemble classifier.

class

the classes predicted by the ensemble classifier.

conf

the confusion matrix which compares the real versus predicted class memberships, based on the class object. Obtains value NULL if the testdata is unlabeled.

Author(s)

Koen W. De Bock [e-mail], Kristof Coussement [e-mail] and Dirk Van den Poel [e-mail]

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.

See Also

GAMens, GAMens.cv

Examples

## Load data, mlbench library should be loaded!)
library(mlbench)
data(Sonar)
SonarSub<-Sonar[,c("V1","V2","V3","V4","V5","V6","Class")]

## Select indexes for training set observations
idx <- c(sample(1:97,60),sample(98:208,70))

## Train GAMrsm using all variables in Sonar dataset. Generate predictions
## for test set observations.
Sonar.GAMrsm <- GAMens(Class~.,SonarSub[idx,], autoform=TRUE, iter=10,
bagging=FALSE, rsm=TRUE)
Sonar.GAMrsm.predict <- predict(Sonar.GAMrsm,SonarSub[-idx,])


## Load data mlbench library should be loaded!)
library(mlbench)
data(Ionosphere)
IonosphereSub<-Ionosphere[,c("V1","V2","V3","V4","V5","V6","V7","V8","Class")]
Ionosphere_s <- IonosphereSub[order(IonosphereSub$Class),]

## Select indexes for training set observations
idx <- c(sample(1:97,60),sample(98:208,70))


## Compare test set classification performance of GAMens, GAMrsm and
## GAMbag ensembles, using using 4 nonparametric terms and 2 linear terms in the
## Ionosphere dataset
Ionosphere.GAMens <- GAMens(Class~s(V3,4)+s(V4,4)+s(V5,3)+s(V6,5)+V7+V8,
IonosphereSub[idx,], autoform=FALSE, iter=10, bagging=TRUE, rsm=TRUE)

Ionosphere.GAMens.predict <- predict(Ionosphere.GAMens,
IonosphereSub[-idx,])

Ionosphere.GAMrsm <- GAMens(Class~s(V3,4)+s(V4,4)+s(V5,3)+s(V6,5)+V7+V8,
IonosphereSub[idx,], autoform=FALSE, iter=10, bagging=FALSE, rsm=TRUE)

Ionosphere.GAMrsm.predict <- predict(Ionosphere.GAMrsm,
IonosphereSub[-idx,])

Ionosphere.GAMbag <- GAMens(Class~s(V3,4)+s(V4,4)+s(V5,3)+s(V6,5)+V7+V8,
IonosphereSub[idx,], autoform=FALSE, iter=10, bagging=TRUE, rsm=FALSE)

Ionosphere.GAMbag.predict <- predict(Ionosphere.GAMbag,
IonosphereSub[-idx,])

## Calculate AUCs(for function colAUC, load caTools library)
library(caTools)
GAMens.auc <- colAUC(Ionosphere.GAMens.predict[[1]],
IonosphereSub[-idx,"Class"]=="good", plotROC=FALSE)

GAMrsm.auc <- colAUC(Ionosphere.GAMrsm.predict[[1]],
Ionosphere[-idx,"Class"]=="good", plotROC=FALSE)

GAMbag.auc <- colAUC(Ionosphere.GAMbag.predict[[1]],
IonosphereSub[-idx,"Class"]=="good", plotROC=FALSE)