ladboost: Gradient tree boosting with least absolute deviation (LAD) loss

Description

A poor-man's implementation of stochastic gradient tree boosting with LAD loss.

Print basic information about a fitted "ladboost" object.

Compute predictions from an "ladboost" object using new data.

Usage

ladboost(
  X,
  y,
  ntree = 100,
  shrinkage = 0.1,
  depth = 6,
  subsample = 0.5,
  init = median(y)
)
# S3 method for ladboost
print(x, ...)
# S3 method for ladboost
predict(object, newdata, ntree = NULL, individual = FALSE, ...)

Value

An object of class "ladboost" which is just a list with the following components:

trees A list of length ntree containing the individual rpart tree fits.
shrinkage The corresponding shrinkage parameter.
depth The maximum depth of each tree.
subsample The (row) subsampling rate.
init The initial constant fit.

A vector (individual = TRUE) or matrix (individual = FALSE) of predictions.

Arguments

X: A data frame of only predictors.
y: A vector of response values
ntree: Integer specifying the number of trees in the ensemble to use. Defaults to using all the trees in the ensemble.
shrinkage: Numeric specifying the shrinkage factor.
depth: Integer specifying the depth of each tree.
subsample: Numeric specifying the proportion of the training data to randomly sample before building each tree. Default is 0.5.
init: Numeric specifying the initial value to boost from. Defaults to the median response (i.e., median(y)).
x: An object of class "ladboost".
...: Additional optional arguments. (Currently ignored.)
object: An object of class "ladboost".
newdata: Data frame of new observations for making predictions.
individual: Logical indicating whether or not to return the (shrunken) predictions from each tree individually (TRUE) or the overall ensemble prediction (FALSE). Default is FALSE.

Examples

Run this code

# Simulate data from the Friedman 1 benchmark problem
set.seed(1025)  # for reproducibility
trn <- gen_friedman1(500)  # training data
tst <- gen_friedman1(500)  # test data

# Gradient boosted decision trees
set.seed(1027)  # for reproducibility
bst <- ladboost(subset(trn, select = -y), y = trn$y, depth = 2)
pred <- predict(bst, newdata = tst)
mean((pred - tst$y) ^ 2)

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