#' @export
makeRLearner.regr.gbm = function() {
makeRLearnerRegr(
cl = "regr.gbm",
package = "gbm",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "distribution", default = "gaussian", values = c("gaussian", "laplace", "poisson", "tdist")),
# FIXME default for distribution in gbm() is bernoulli
makeIntegerLearnerParam(id = "n.trees", default = 100L, lower = 1L),
makeIntegerLearnerParam(id = "cv.folds", default = 0L),
makeIntegerLearnerParam(id = "interaction.depth", default = 1L, lower = 1L),
makeIntegerLearnerParam(id = "n.minobsinnode", default = 10L, lower = 1L),
makeNumericLearnerParam(id = "shrinkage", default = 0.001, lower = 0),
makeNumericLearnerParam(id = "bag.fraction", default = 0.5, lower = 0, upper = 1),
makeNumericLearnerParam(id = "train.fraction", default = 1, lower = 0, upper = 1),
makeLogicalLearnerParam(id = "keep.data", default = TRUE, tunable = FALSE),
makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)
),
par.vals = list(distribution = "gaussian", keep.data = FALSE),
properties = c("missings", "numerics", "factors", "weights", "featimp"),
name = "Gradient Boosting Machine",
short.name = "gbm",
note = '`keep.data` is set to FALSE to reduce memory requirements, `distribution` has been set to `"gaussian"` by default.',
callees = "gbm"
)
}
#' @export
trainLearner.regr.gbm = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
if (is.null(.weights)) {
f = getTaskFormula(.task)
gbm::gbm(f, data = getTaskData(.task, .subset), ...)
} else {
f = getTaskFormula(.task)
gbm::gbm(f, data = getTaskData(.task, .subset), weights = .weights, ...)
}
}
#' @export
predictLearner.regr.gbm = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
gbm::predict.gbm(m, newdata = .newdata, n.trees = length(m$trees), ...)
}
#' @export
getFeatureImportanceLearner.regr.gbm = function(.learner, .model, ...) {
getFeatureImportanceLearner.classif.gbm(.learner, .model, ...)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.