leaf_posterior

This function returns a posterior distribution on leaves, conditional on some
evidence.

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

leaf_posterior function

<dl><dt>params</dt>
<dd>Circuit parameters learned via <code>forde</code>.</dd>
<dt>evidence</dt>
<dd>Data frame of conditioning event(s).</dd></dl>

Arguments

Compute leaf posterior — leaf_posterior

<dl>

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


<dt>evidence</dt>
<dd>Data frame of conditioning event(s).</dd>

</dl>

leaf_posterior: Compute leaf posterior

Description

Usage

Arguments