Control Forest Hyper Parameters {party}R Documentation

Control for Conditional Tree Forests

Description

Various parameters that control aspects of the `cforest' fit via its `control' argument.

Usage

cforest_unbiased(...)
cforest_classical(...)
cforest_control(teststat = "max",
                testtype = "Teststatistic",
                mincriterion = qnorm(0.9),
                savesplitstats = FALSE,
                ntree = 500, mtry = 5, replace = TRUE,
                fraction = 0.632, ...)

Arguments

teststat a character specifying the type of the test statistic to be applied.
testtype a character specifying how to compute the distribution of the test statistic.
mincriterion the value of the test statistic or 1 - p-value that must be exceeded in order to implement a split.
mtry number of input variables randomly sampled as candidates at each node for random forest like algorithms. The default mtry = 0 means that no random selection takes place and bagging is performed.
savesplitstats a logical determining if the process of standardized two-sample statistics for split point estimate is saved for each primary split.
ntree number of trees to grow in a forest.
replace a logical indicating whether sampling of observations is done with or without replacement.
fraction fraction of number of observations to draw without replacement (only relevant if replace = TRUE).
... additional arguments to be passed to ctree_control.

Details

All three functions return an object of class ForestControl-class defining hyper parameters to be specified via the control argument of cforest.

The arguments teststat, testtype and mincriterion determine how the global null hypothesis of independence between all input variables and the response is tested (see ctree). The argument nresample is the number of Monte-Carlo replications to be used when testtype = "MonteCarlo".

A split is established when the sum of the weights in both daugther nodes is larger than minsplit, this avoids pathological splits at the borders. When stump = TRUE, a tree with at most two terminal nodes is computed.

The argument mtry > 0 means that a random forest like `variable selection', i.e., a random selection of mtry input variables, is performed in each node.

It might be informative to look at scatterplots of input variables against the standardized two-sample split statistics, those are available when savesplitstats = TRUE. Each node is then associated with a vector those length is determined by the number of observations in the learning sample and thus much more memory is required.

Function cforest_unbiased returns the settings suggested for the construction of unbiased random forests by Strobl et al. (2007) and is the default since version 0.9-90. Hyper parameter settings mimicing the behaviour of randomForest are available in cforest_classical which have been used as default up to version 0.9-14.

Value

An object of class ForestControl-class.

References

Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis and Torsten Hothorn (2007). Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics, 8, 25. http://www.BioMedCentral.com/1471-2105/8/25/


[Package party version 0.9-91 Index]