# RandomForest-class

##### Class "RandomForest"

A class for representing random forest ensembles.

- Keywords
- classes

##### Objects from the Class

Objects can be created by calls of the form `new("RandomForest", ...)`

.

##### Slots

`ensemble`

:Object of class

`"list"`

, each element being an object of class`"'>BinaryTree"`

.`data`

:`initweights`

:a vector of initial weights.

`weights`

:a list of weights defining the sub-samples.

`where`

:a matrix of integers vectors of length n (number of observations in the learning sample) giving the number of the terminal node the corresponding observations is element of (in each tree).

`data`

:`responses`

:an object of class

`"VariableFrame"`

storing the values of the response variable(s).`cond_distr_response`

:a function computing the conditional distribution of the response.

`predict_response`

:a function for computing predictions.

`prediction_weights`

:a function for extracting weights from terminal nodes.

`get_where`

:a function for determining the number of terminal nodes observations fall into.

`update`

:a function for updating weights.

##### Methods

- treeresponse
`signature(object = "RandomForest")`

: ...- weights
`signature(object = "RandomForest")`

: ...- where
`signature(object = "RandomForest")`

: ...

##### Examples

```
# NOT RUN {
set.seed(290875)
### honest (i.e., out-of-bag) cross-classification of
### true vs. predicted classes
data("mammoexp", package = "TH.data")
table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp,
control = cforest_unbiased(ntree = 50)),
OOB = TRUE))
# }
```

*Documentation reproduced from package party, version 1.3-3, License: GPL-2*