honestRFTree-class: honstRFTree Constructor

Description Arguments Format

Description

'honestRFTree' inherits 'RFTree', which serves as a modified version of 'RFTree'. The major change is that when 'honestRFTree' determines a valid split point, it looks at whether or not it is valid for both averaging and splitting dataset.

Create a honestRFTree by making specifc observatios as splitting and averaging dataset.

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.

nodesize

The minimum observations contained in terminal nodes. The default value is 5.

sampleIndex

A list of the index of observations that are used as averaging dataset. The index are based on the original dataset 'x' and 'y' from forest. Essentially, 'x[sampleIndex]' generates the whole splitting dataset.

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.

Format

An object of class NULL of length 0.


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