grf (version 0.9.5)

custom_forest: Custom forest

Description

Trains a custom forest model.

Usage

custom_forest(X, Y, sample.fraction = 0.5, mtry = NULL, num.trees = 2000,
  num.threads = NULL, min.node.size = NULL, honesty = TRUE,
  alpha = 0.05, seed = NULL)

Arguments

X

The covariates used in the regression.

Y

The outcome.

sample.fraction

Fraction of the data used to build each tree. Note: If honesty is used, these subsamples will further be cut in half.

mtry

Number of variables tried for each split.

num.trees

Number of trees grown in the forest. Note: Getting accurate confidence intervals generally requires more trees than getting accurate predictions.

num.threads

Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.

min.node.size

A target for the minimum number of observations in each tree leaf. Note that nodes with size smaller than min.node.size can occur, as in the original randomForest package.

honesty

Whether or not honest splitting (i.e., sub-sample splitting) should be used.

alpha

Maximum imbalance of a split.

seed

The seed for the C++ random number generator.

...

Additional arguments (currently ignored).

Value

A trained regression forest object.

Examples

# NOT RUN {
# Train a custom forest.
n = 50; p = 10
X = matrix(rnorm(n*p), n, p)
Y = X[,1] * rnorm(n)
c.forest = custom_forest(X, Y)

# Predict using the forest.
X.test = matrix(0, 101, p)
X.test[,1] = seq(-2, 2, length.out = 101)
c.pred = predict(c.forest, X.test)
# }
# NOT RUN {
# }