create_dot_body

Writes each node information
If it is a leaf node: show it in different color, show number of samples, show leaf id
If it is a non-leaf node: show its splitting variable and splitting value
If trained with missing values, the edge arrow is filled according to which direction the NAs are sent.

internal

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

create_dot_body function

<dl><dt>tree</dt>
<dd>the tree to convert</dd>
<dt>index</dt>
<dd>the index of the current node</dd>
<dt>include.na.path</dt>
<dd>A boolean toggling whether to include the path of missing values or not.</dd></dl>

Arguments

Writes each node information
If it is a leaf node: show it in different color, show number of samples, show leaf id
If it is a non-leaf node: show its splitting variable and splitting value
If trained with missing values, the edge arrow is filled according to which direction the NAs are sent. — create_dot_body

<dl>

<dt>tree</dt>
<dd>the tree to convert</dd>


<dt>index</dt>
<dd>the index of the current node</dd>


<dt>include.na.path</dt>
<dd>A boolean toggling whether to include the path of missing values or not.</dd>

</dl>

Writes each node information
If it is a leaf node: show it in different color, show number of samples, show leaf id
If it is a non-leaf node: show its splitting variable and splitting value
If trained with missing values, the edge arrow is filled according to which direction the NAs are sent.

Description

Usage

Arguments