Deforesting a random forest

The main purpose of this function is to allow for post-processing of ensembles via L2 regularized regression (i.e., the LASSO), as described in Friedman and Popescu (2003). The basic idea is to use the LASSO to post-process the predictions from the individual base learners in an ensemble (i.e., decision trees) in the hopes of producing a much smaller model without sacrificing much in the way of accuracy, and in some cases, improving it. Friedman and Popescu (2003) describe conditions under which tree-based ensembles, like random forest, can potentially benefit from such post-processing (e.g., using shallower trees trained on much smaller samples of the training data without replacement). However, the computational benefits of such post-processing can only be realized if the base learners "zeroed out" by the LASSO can actually be removed from the original ensemble, hence the purpose of this function. A complete example using ranger can be found at https://github.com/imbs-hl/ranger/issues/568.

Usage

deforest(object, which.trees = NULL, ...)

# S3 method for class 'ranger'
deforest(object, which.trees = NULL, warn = TRUE, ...)

Arguments

object

A fitted random forest (e.g., a ranger object).

which.trees

Vector giving the indices of the trees to remove.

...

Additional (optional) arguments. (Currently ignored.)

warn

Logical indicating whether or not to warn users that some of the standard output of a typical ranger object or no longer available after deforestation. Default is TRUE.

Value

An object of class "deforest.ranger"; essentially, a ranger object with certain components replaced with NAs (e.g., out-of-bag (OOB) predictions, variable importance scores (if requested), and OOB-based error metrics).

Note

This function is a generic and can be extended by other packages.

References

Friedman, J. and Popescu, B. (2003). Importance sampled learning ensembles, Technical report, Stanford University, Department of Statistics. https://jerryfriedman.su.domains/ftp/isle.pdf.

Author

Brandon M. Greenwell

Examples

## Example of deforesting a random forest
rfo <- ranger(Species ~ ., data = iris, probability = TRUE, num.trees = 100)
dfo <- deforest(rfo, which.trees = c(1, 3, 5))
#> Warning: Many of the components of a typical "ranger" object are not available after deforestation and are instead replaced with `NA` (e.g., out-of-bag (OOB) predictions, variable importance scores (if requested), and OOB-based error metrics).
dfo  # same as `rfo` but with trees 1, 3, and 5 removed
#> Ranger (deforested) result
#> 
#> Note that many of the components of a typical "ranger" object are not available after deforestation and are instead replaced with `NA` (e.g., out-of-bag (OOB) predictions, variable importance scores (if requested), and OOB-based error metrics) 
#> 
#> Type:                             Probability estimation 
#> Number of trees:                  97 
#> Sample size:                      150 
#> Number of independent variables:  4 
#> Mtry:                             2 
#> Target node size:                 10 
#> Variable importance mode:         none 
#> Splitrule:                        gini 
#> OOB prediction error (Brier s.):  NA 

## Sanity check
preds.rfo <- predict(rfo, data = iris, predict.all = TRUE)$predictions
preds.dfo <- predict(dfo, data = iris, predict.all = TRUE)$predictions
identical(preds.rfo[, , -c(1, 3, 5)], y = preds.dfo)
#> [1] TRUE