# Predicts from a fitted GAMens object (i.e., GAMbag, GAMrsm or GAMens classifier). Generates predictions (classes and class membership probabilities) for observations in a dataframe using a GAMens object (i.e., GAMens, GAMrsm or GAMbag classifier). ```r ## 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. ## Returns 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. ## Examples ```r ## 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) ``` ## 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` ## Author(s) Koen W. De Bock kdebock@audencia.com , Kristof Coussement K.Coussement@ieseg.fr and Dirk Van den Poel Dirk.VandenPoel@ugent.be

predict.GAMens function