grf: tune_ll_regression_forest – R documentation

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grf

tune_ll_regression_forest

Local linear forest tuning

Description

Finds the optimal ridge penalty for local linear prediction.

Usage

tune_ll_regression_forest(
  forest,
  linear.correction.variables = NULL,
  ll.weight.penalty = FALSE,
  num.threads = NULL,
  lambda.path = NULL
)

Arguments

`forest`	The forest used for prediction.
`linear.correction.variables`	Variables to use for local linear prediction. If left null, all variables are used. Default is NULL.
`ll.weight.penalty`	Option to standardize ridge penalty by covariance (TRUE), or penalize all covariates equally (FALSE). Defaults to FALSE.
`num.threads`	Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.
`lambda.path`	Optional list of lambdas to use for cross-validation.

Value

A list of lambdas tried, corresponding errors, and optimal ridge penalty lambda.

Examples

# Find the optimal tuning parameters.
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
forest <- regression_forest(X, Y)
tuned.lambda <- tune_ll_regression_forest(forest)

# Use this parameter to predict from a local linear forest.
predictions <- predict(forest, linear.correction.variables = 1:p,
                       ll.lambda = tuned.lambda$lambda.min)

grf

Generalized Random Forests

v1.2.0

GPL-3

Authors

Julie Tibshirani [aut, cre], Susan Athey [aut], Rina Friedberg [ctb], Vitor Hadad [ctb], David Hirshberg [ctb], Luke Miner [ctb], Erik Sverdrup [ctb], Stefan Wager [aut], Marvin Wright [ctb]

Initial release