Description Usage Arguments Value Author(s) References See Also Examples
View source: R/Predict.esknnClass.R
Classification prediction for test data on the trained esknnClass
object for.
1 | Predict.esknnClass(optModels, xtest, ytest=NULL, k = NULL)
|
optModels |
An object of esknnClass |
xtest |
A matrix or data frame test set features/attributes. |
ytest |
Optional: A vector of lenth |
k |
Number of nearest neighbors considered. The same value is considered as for training in |
predClass |
A vector of predicted classes of test set observations. |
ConfMatrix |
Confusion matrix return a matrix of cross classification counts based on the estimated class labels and the true class labels of test observations. This matrix is returned if ytest is given. |
ClassError |
Classification error rate of the clssifier for test set observations. This is returned if ytest is provided. |
Asma Gul <agul@essex.ac.uk>
Gul, A., Perperoglou, A., Khan, Z., Mahmoud, O.,Miftahuddin, M., Adler, W. and Lausen, B.(2014),Ensemble of Subset of kNN Classifiers, Journal name to appear.
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 | # Load the data
data(hepatitis)
data <- hepatitis
# Spliting the data into testing and training parts.
Class <- data[,names(data)=="Class"]
data$Class<-as.factor(as.numeric(Class)-1)
train <- data[sample(1:nrow(data),0.7*nrow(data)),]
test <- data[-(sample(1:nrow(data),0.7*nrow(data))),]
ytrain<-train[,names(train)=="Class"]
xtrain<-train[,names(train)!="Class"]
xtest<-test[,names(test)!="Class"]
ytest <- test[,names(test)=="Class"]
# Trian esknnClass using training data
model<-esknnClass(xtrain, ytrain,k=NULL)
# Predict on test data
resClass<-Predict.esknnClass(model,xtest,ytest,k=NULL)
# Returning Objects are predicted class labels, confusion matrix and classification error
resClass$predClass
resClass$ConfMatrix
resClass$ClassError
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