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
deforest(object, which.trees = NULL, ...) ## S3 method for class 'ranger' deforest(object, which.trees = NULL, warn = TRUE, ...)
A fitted random forest (e.g., a
Vector giving the indices of the trees to remove.
Additional (optional) arguments. (Currently ignored.)
Logical indicating whether or not to warn users that some of the
standard output of a typical
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).
This function is a generic and can be extended by other packages.
Brandon M. Greenwell
Friedman, J. and Popescu, B. (2003). Importance sampled learning ensembles, Technical report, Stanford University, Department of Statistics. https://jerryfriedman.su.domains/ftp/isle.pdf.
## 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)) dfo # same as `rfo` but with trees 1, 3, and 5 removed ## 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)
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