custom_forest: Custom forest

Description Usage Arguments Value Examples

View source: R/custom_forest.R

Description

Trains a custom forest model.

Usage

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custom_forest(X, Y, sample.fraction = 0.5, mtry = NULL, num.trees = 2000,
  num.threads = NULL, min.node.size = NULL, honesty = TRUE,
  honesty.fraction = NULL, alpha = 0.05, imbalance.penalty = 0,
  seed = NULL, clusters = NULL, samples_per_cluster = 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.

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 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.

seed

The seed for the C++ random number generator.

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. Must be less than the size of the smallest cluster. If set to NULL software will set this value to the size of the smallest cluster.

Value

A trained regression forest object.

Examples

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## 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)

## End(Not run)

grf documentation built on Sept. 24, 2018, 5:04 p.m.

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