predict.gausspr function

predict method for Gaussian Processes object

Prediction of test data using Gaussian Processes

## S4 method for signature 'gausspr'
predict(object, newdata, type = "response", coupler = "minpair")

Arguments

• object: an S4 object of class gausspr created by the gausspr function

• newdata: a data frame or matrix containing new data

• type: one of response, probabilities

  indicating the type of output: predicted values or matrix of class probabilities

• coupler: Coupling method used in the multiclass case, can be one of minpair or pkpd (see reference for more details).

Returns

• response: predicted classes (the classes with majority vote) or the response value in regression.

• probabilities: matrix of class probabilities (one column for each class and one row for each input).

References

• C. K. I. Williams and D. Barber

  Bayesian classification with Gaussian processes.

  IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1342-1351, 1998

  https://homepages.inf.ed.ac.uk/ckiw/postscript/pami_final.ps.gz

• T.F. Wu, C.J. Lin, R.C. Weng.

  Probability estimates for Multi-class Classification by Pairwise Coupling

  https://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf

Author(s)

Alexandros Karatzoglou

alexandros.karatzoglou@ci.tuwien.ac.at

Examples

## example using the promotergene data set
data(promotergene)

## create test and training set
ind <- sample(1:dim(promotergene)[1],20)
genetrain <- promotergene[-ind, ]
genetest <- promotergene[ind, ]

## train a support vector machine
gene <- gausspr(Class~.,data=genetrain,kernel="rbfdot",
                kpar=list(sigma=0.015))
gene

## predict gene type probabilities on the test set
genetype <- predict(gene,genetest,type="probabilities")
genetype