OTE (version 1.0.1)

Predict.OTClass: Prediction function for the object returned by OTClass

Description

This function provides prediction for test data on the trained OTClass object for classification.

Usage

Predict.OTClass(Opt.Trees, XTesting, YTesting)

Arguments

Opt.Trees

An object of class OptTreesEns.

XTesting

An m x d dimensional training data matrix/frame consiting of test observations where m is the number of observations and d is the number of features.

YTesting

Optional. A vector of length m consisting of class labels for the test data. Should be binary (0,1).

Value

A list with values

Error.Rate

Error rate of the clssifier for the observations in XTesting.

Confusion.Matrix

Confusion matrix based on the estimated class labels and the true class labels.

Estimated.Class

A vector of length m consisting of the estimated class labels for the observations in XTesting.

References

Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). Ensemble of optimal trees, random forest and random projection ensemble classification. Advances in Data Analysis and Classification, 1-20.

Liaw, A. and Wiener, M. (2002) ``Classification and regression by random forest'' R news. 2(3). 18--22.

See Also

OTClass, OTReg, OTProb

Examples

# NOT RUN {
#load the data

  data(Body)
  data <- Body

#Divide the data into training and test parts

  set.seed(9123)
  n <- nrow(data)
  training <- sample(1:n,round(2*n/3))
  testing <- (1:n)[-training]
  X <- data[,1:24]
  Y <- data[,25]

#Train OTClass on the training data

  Opt.Trees <- OTClass(XTraining=X[training,],YTraining = Y[training],
  t.initial=200, method="oob+independent")

#Predict on test data

  Prediction <- Predict.OTClass(Opt.Trees, X[testing,],YTesting=Y[testing])

#Objects returned

  names(Prediction)
  Prediction$Confusion.Matrix
  Prediction$Predicted.Class.Labels

# }