R: Local linear forest tuning

tune_ll_regression_forest {grf} | R Documentation

## 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.
`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_ll_regression_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 0.10.3 Index]