emil.fit.cforest

Dataset, observations as rows and descriptors as columns.

formula

Which control function to use, see <code><a href="/link/cforest_control?package=emil&version=1.1-6&to=party" rd-options="party" data-mini-rdoc="party::cforest_control">cforest_control</a></code>.

ctrl.fun

Sent to the function specified by <code>ctrl.fun</code>.

A <code><a href="/link/cforest?package=emil&version=1.1-6&to=party" rd-options="party" data-mini-rdoc="party::cforest">cforest</a></code> is a random forest based on conditional inference
trees, using the implementation in the <code>party</code> package.
These trees can be used for classification, regression or survival
analysis, but only the survival part has been properly tested so far.

A toolbox for designing and evaluating predictive models with
    resampling methods. The aim of this package is to provide a simple and
    efficient general framework for working with any type of prediction
    problem, be it classification, regression or survival analysis, that is
    easy to extend and adapt to your specific setting. Some commonly used
    methods for classification, regression and survival analysis are included.

emil.fit.cforest: Fit conditional inference forest

Description

Usage

Arguments

Value

Details

See Also