Description Objects from the Class Slots Methods Examples

A class for representing random forest ensembles.

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

.

`ensemble`

:Object of class

`"list"`

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

.`data`

:an object of class

`"ModelEnv"`

.`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`

:an object of class

`"ModelEnv"`

.`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.

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

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

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

: ...

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