fit_cforest: Fit conditional inference forest
In emil: Evaluation of Modeling without Information Leakage

Description Usage Arguments Details Value Author(s) References See Also

A cforest is a random forest based on conditional inference trees, using the implementation in the party package. These trees can be used for classification, regression or survival analysis, but only the survival part has been properly tested so far.

1	fit_cforest(x, y, formula = y ~ ., ctrl_fun = party::cforest_unbiased, ...)

`x`	Dataset, observations as rows and descriptors as columns.
`y`	Responses.
`formula`	Formula linking response to descriptors.
`ctrl_fun`	Which control function to use, see `cforest_control`.
`...`	Sent to the function specified by `ctrl_fun`.

The parameters to cforest are set using a cforest_control object. You should read the documentation as the default values are chosen for technical reasons, not predictive performance! Pay special attention to mtry which is set very low by default.

A fitted cforest model.

Christofer Bäcklin

Torsten Hothorn, Peter Buehlmann, Sandrine Dudoit, Annette Molinaro and Mark Van Der Laan (2006). Survival Ensembles. Biostatistics, 7(3), 355–373.

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). URL http://www.biomedcentral.com/1471-2105/8/25.

Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin and Achim Zeileis (2008). Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9(307). URL http://www.biomedcentral.com/1471-2105/9/307.

emil, predict_cforest, modeling_procedure

emil documentation built on Aug. 1, 2018, 1:03 a.m.