print.tuning_output

Print tuning output.
Displays average error for q-quantiles of tuned parameters.

Forest-based statistical estimation and inference.
GRF provides non-parametric methods for heterogeneous treatment effects estimation
(optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables),
as well as least-squares regression, quantile regression, and survival regression,
all with support for missing covariates.

Erik Sverdrup

Generalized Random Forests

Julie Tibshirani

Susan Athey

Rina Friedberg

Vitor Hadad

David Hirshberg

Luke Miner

Stefan Wager

Marvin Wright

print.tuning_output function

<dl><dt>x</dt>
<dd>The tuning output to print.</dd>
<dt>tuning.quantiles</dt>
<dd>vector of quantiles to display average error over.
Default: seq(0, 1, 0.2) (quintiles)</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Print tuning output.
Displays average error for q-quantiles of tuned parameters. — print.tuning_output

<dl>

<dt>x</dt>
<dd>The tuning output to print.</dd>


<dt>tuning.quantiles</dt>
<dd>vector of quantiles to display average error over.
Default: seq(0, 1, 0.2) (quintiles)</dd>


<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd>

</dl>

print.tuning_output: Print tuning output. Displays average error for q-quantiles of tuned parameters.

Description

Usage

Arguments