cforest {party} | R Documentation

## Random Forest

### Description

An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners.

### Usage

    cforest(formula, data = list(), subset = NULL, weights = NULL,
            controls = cforest_control(),
            xtrafo = ptrafo, ytrafo = ptrafo, scores = NULL)

### Arguments

`formula` | a symbolic description of the model to be fit.
`data` | an data frame containing the variables in the model.
`subset` | an optional vector specifying a subset of observations to be used in the fitting process.
`weights` | an optional vector of weights to be used in the fitting process. Only non-negative integer valued weights are allowed.
`controls` | an object of class `ForestControl-class`, which can be obtained using `cforest_control`.
`xtrafo` | a function to be applied to all input variables. By default, the `ptrafo` function is applied.
`ytrafo` | a function to be applied to all response variables. By default, the `ptrafo` function is applied.
`scores` | an optional named list of scores to be attached to ordered factors.

### Details

This implementation of the random forest (and bagging) algorithm differs from the reference implementation in `randomForest]` with respect to the base learner used and the aggregation scheme applied.

Conditional inference trees, see `ctree`, are fitted to each of the `B` bootstrap samples of the learning sample. There are many hyper parameters that can be controlled, see `cforest_control`. You MUST NOT change anything you don't understand completely.

The aggregation scheme works by averaging observation weights extracted from each of the `B` trees and NOT by averaging predictions directly. See Hothorn et al. (2004) for a description.

Ensembles of conditional inference trees have not yet been extensively tested, so this routine is meant for the expert user only and its current state is rather experimental. However, there are some things that can't be done with `randomForest`, for example fitting forests to censored response variables.

By default, raw test statitics are maximized and five inputs are randomly examined for possible splits in each node.

### Value

An object of class `RandomForest-class`.

### References

Leo Breiman (2001). Random Forests. Machine Learning, 45(1), 5–32.

Torsten Hothorn, Berthold Lausen, Axel Benner and Martin Radespiel-Troeger (2004). Bagging Survival Trees. Statistics in Medicine, 23(1), 77–91.

### Examples

        ### honest (i.e., out-of-bag) cross-classification of
        ### true vs. predicted classes
        table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp,
                                   control = cforest_control(ntree = 50)),
                                   OOB = TRUE))

        ### fit forest to censored response
        if (require("ipred")) {

            data("GBSG2", package = "ipred")
            bst <- cforest(Surv(time, cens) ~ ., data = GBSG2,
                       control = cforest_control(ntree = 50))

            ### estimate conditional Kaplan-Meier curves
            treeresponse(bst, newdata = GBSG2[1:2,], OOB = TRUE)

            ### if you can't resist to look at individual trees ...
            party:::prettytree(bst@ensemble[[1]], names(bst@data@get("input")))
        }

[Package party version 0.8-6 Index]