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

In OTE: Optimal Trees Ensembles for Regression, Classification and Class Membership Probability Estimation

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.

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

OTClass, OTReg, 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 <- 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

OTE documentation built on April 20, 2020, 5:05 p.m.