LogitBoost {caTools}R Documentation

LogitBoost Classification Algorithm


Train logitboost classification algorithm using decision stumps (one node decision trees) as weak learners.


LogitBoost(xlearn, ylearn, nIter=ncol(xlearn))



A matrix or data frame with training data. Rows contain samples and columns contain features


Class labels for the training data samples. A response vector with one label for each row/component of xlearn. Can be either a factor, string or a numeric vector.


An integer, describing the number of iterations for which boosting should be run, or number of decision stumps that will be used.


The function was adapted from logitboost.R function written by Marcel Dettling. See references and "See Also" section. The code was modified in order to make it much faster for very large data sets. The speed-up was achieved by implementing a internal version of decision stump classifier instead of using calls to rpart. That way, some of the most time consuming operations were precomputed once, instead of performing them at each iteration. Another difference is that training and testing phases of the classification process were split into separate functions.


An object of class "LogitBoost" including components:


List of decision stumps (one node decision trees) used:

  • column 1: feature numbers or each stump, or which column each stump operates on

  • column 2: threshold to be used for that column

  • column 3: bigger/smaller info: 1 means that if values in the column are above threshold than corresponding samples will be labeled as lablist[1]. Value "-1" means the opposite.

If there are more than two classes, than several "Stumps" will be cbind'ed


names of each class


Jarek Tuszynski (SAIC) jaroslaw.w.tuszynski@saic.com


Dettling and Buhlmann (2002), Boosting for Tumor Classification of Gene Expression Data.

See Also


  Data  = iris[,-5]
  Label = iris[, 5]
  # basic interface
  model = LogitBoost(Data, Label, nIter=20)
  Lab   = predict(model, Data)
  Prob  = predict(model, Data, type="raw")
  t     = cbind(Lab, Prob)
  t[1:10, ]

  # two alternative call syntax
  pp=p[!is.na(p)]; qq=q[!is.na(q)]
  stopifnot(pp == qq)

  # accuracy increases with nIter (at least for train set)
  table(predict(model, Data, nIter= 2), Label)
  table(predict(model, Data, nIter=10), Label)
  table(predict(model, Data),           Label)
  # example of spliting the data into train and test set
  mask = sample.split(Label)
  model = LogitBoost(Data[mask,], Label[mask], nIter=10)
  table(predict(model, Data[!mask,], nIter=2), Label[!mask])
  table(predict(model, Data[!mask,]),          Label[!mask])

[Package caTools version 1.18.2 Index]