R: Local linear forest tuning

tune_local_linear_forest {grf}

R Documentation

Local linear forest tuning

Description

Finds the optimal ridge penalty for local linear prediction.

Usage

tune_local_linear_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.
`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

## Not run: 
# 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_local_linear_forest(forest)

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

## End(Not run)

[Package grf version 0.10.2 Index]