recursivePartition-RFTree: recursivePartition-RFTree

Description Usage Arguments Value

Description

Grow the decision tree by recursively finding the best split feature and value.

Usage

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recursivePartition(x, y, se, mtry = max(floor(ncol(x)/3), 1),
  sampleIndex = list(averagingSampleIndex = 1:length(y), splittingSampleIndex
  = 1:length(y)), nodesize = list(splittingNodeSize = 5, averagingNodeSize =
  5), splitrule = "variance", categoricalFeatureCols = list())

Arguments

x

A data frame of all training predictors.

y

A vector of all training responses.

mtry

The number of variables randomly selected at each split point. The default value is set to be one third of total number of features of the training data.

sampleIndex

A list of index of dataset used in this node and its children. 'sampleIndex' contains two keys 'averagingSampleIndex' and 'splittingSampleIndex'. 'averagingSampleIndex' is used to generate aggregated prediction for the node. 'splittingSampleIndex' is used for 'honestRF' which stores the splitting data when creating the tree. In default, 'splittingSampleIndex' is the same as 'averagingSampleIndex'.

nodesize

The minimum observations contained in terminal nodes. This parameter is actually a list containing the values for both 'splittingNodeSize' and 'averagingNodeSize'.

splitrule

A string to specify how to find the best split among all candidate feature values. The current version only supports 'variance' which minimizes the overall MSE after splitting. The default value is 'variance'.

categoricalFeatureCols

A list of index for all categorical data. Used for trees to detect categorical columns.

Value

root node.


soerenkuenzel/hte documentation built on June 12, 2018, 4:26 p.m.