Predict.OTProb {OTE}

Prediction function for the object returned by OTProb

Description

This function provides prediction for test data on the trained OTProb object for class membership probability estimation.

Usage

Predict.OTProb(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

Brier.Score

Brier Score based on the estimated probabilities and true class label in YTesting.

Estimated.Probabilities

A vector of length m consisting of the estimated class membership probabilities for the observation in XTesting

Author(s)

Zardad Khan <[e-mail]>

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

OTProb.

Examples

#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 <- OTProb(XTraining=X[training,],YTraining = Y[training],t.initial=200)
  
#Predict on test data

  Prediction <- Predict.OTProb(Opt.Trees, X[testing,],YTesting=Y[testing])
  
#Objects returned

  names(Prediction)
  Prediction$Brier.Score
  Prediction$Estimated.Probabilities