prep_evi

This function prepares the evidence for computing leaf posteriors.

Adversarial random forests (ARFs) recursively partition data into
fully factorized leaves, where features are jointly independent. The
procedure is iterative, with alternating rounds of generation and
discrimination. Data becomes increasingly realistic at each round, until
original and synthetic samples can no longer be reliably distinguished.
This is useful for several unsupervised learning tasks, such as density
estimation and data synthesis. Methods for both are implemented in this
package. ARFs naturally handle unstructured data with mixed continuous and
categorical covariates. They inherit many of the benefits of random forests,
including speed, flexibility, and solid performance with default parameters.
For details, see Watson et al. (2022) <arXiv:2205.09435>.

Marvin Wright

Adversarial Random Forests

Marvin N. Wright

David S. Watson

Kristin Blesch

Jan Kapar

prep_evi function

<dl><dt>params</dt>
<dd>Circuit parameters learned via <code>forde</code>.</dd>
<dt>evidence</dt>
<dd>Optional set of conditioning events.</dd></dl>

Arguments

Preprocess evidence — prep_evi

<dl>

<dt>params</dt>
<dd>Circuit parameters learned via <code>forde</code>.</dd>


<dt>evidence</dt>
<dd>Optional set of conditioning events.</dd>

</dl>

prep_evi: Preprocess evidence

Description

Usage

Arguments