Description Usage Arguments Value Author(s) References See Also Examples
Generates predictions (classes and class membership probabilities) for observations in a dataframe using a GAMens object (i.e., GAMens, GAMrsm or GAMbag classifier).
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object |
fitted model object of |
data |
data frame with observations to genenerate predictions for. |
... |
further arguments passed to or from other methods. |
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
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Koen W. De Bock kdebock@audencia.com, Kristof Coussement K.Coussement@ieseg.fr and Dirk Van den Poel Dirk.VandenPoel@ugent.be
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | ## 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)
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