ESKNN (version 1.0)

Predict.esknnProb: Prediction function returning class membership probability estimates

Description

This function provides class membership probability estimates for the test set observations.

Usage

Predict.esknnProb(optModels, xtest, ytest, k = NULL)

Arguments

optModels

An object of class esknnProb.

xtest

A matrix or data frame test set features/attributes.

ytest

Optional: A vector of class labels for the test data. Class labels should be factor of two levels (0,1) represented by variable Class in the data. The Brier score is returned if this vector is given.

k

Number of nearest neighbors considered. The same value should be considered as for training in esknnProb

Value

PredProb

A vector of estimated class membership probabilities of test set observations.

BrierScore

A vector of Brier Score based on the estimated probabilities and true class label of test set observations. This vector is returned if ytest is given.

References

ul, 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.

See Also

esknnProb

Examples

# Load the data

data(sonar)
data <- sonar

# Divide the data into testing and training parts

  Class <- data[,names(data)=="Class"]
  
# Class Varible must be a factor in (0,1) 

  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 esknnProb

  model<-esknnProb(xtrain, ytrain,k=NULL)

# Predict on test data

  resProb<-Predict.esknnProb(model,xtest,ytest,k=NULL)

## Returning Objects

  resProb$PredProb
  resProb$BrierScore