grf (version 0.10.3)

custom_forest: Custom forest

Description

Trains a custom forest model.

Usage

custom_forest(X, Y, sample.fraction = 0.5, mtry = NULL,
  num.trees = 2000, min.node.size = NULL, honesty = TRUE,
  honesty.fraction = NULL, alpha = 0.05, imbalance.penalty = 0,
  clusters = NULL, samples.per.cluster = NULL,
  compute.oob.predictions = TRUE, num.threads = NULL, 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 = TRUE, these subsamples will further be cut by a factor of honesty.fraction.

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.

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 to use honest splitting (i.e., sub-sample splitting).

honesty.fraction

The fraction of data that will be used for determining splits if honesty = TRUE. Corresponds to set J1 in the notation of the paper. When using the defaults (honesty = TRUE and honesty.fraction = NULL), half of the data will be used for determining splits

alpha

A tuning parameter that controls the maximum imbalance of a split.

imbalance.penalty

A tuning parameter that controls how harshly imbalanced splits are penalized.

clusters

Vector of integers or factors specifying which cluster each observation corresponds to.

samples.per.cluster

If sampling by cluster, the number of observations to be sampled from each cluster when training a tree. If NULL, we set samples.per.cluster to the size of the smallest cluster. If some clusters are smaller than samples.per.cluster, the whole cluster is used every time the cluster is drawn. Note that clusters with less than samples.per.cluster observations get relatively smaller weight than others in training the forest, i.e., the contribution of a given cluster to the final forest scales with the minimum of the number of observations in the cluster and samples.per.cluster.

compute.oob.predictions

Whether OOB predictions on training set should be precomputed.

num.threads

Number of threads used in training. By default, the number of threads is set to the maximum hardware concurrency

seed

The seed of the C++ random number generator.

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 {
# }