post_x

This function prepares output data.

Autoencoding Random Forests ('RFAE') provide a method to
autoencode mixed-type tabular data using Random Forests ('RF'), which
involves projecting the data to a latent feature space of user-chosen
dimensionality (usually a lower dimension), and then decoding the latent
representations back into the input space. The encoding stage is useful for
feature engineering and data visualisation tasks, akin to how principal
component analysis ('PCA') is used, and the decoding stage is useful
for compression and denoising tasks. At its core, 'RFAE' is a
post-processing pipeline on a trained random forest model. This means
that it can accept any trained RF of 'ranger' object type: 'RF', 'URF' or
'ARF'. Because of this, it inherits Random Forests' robust performance and
capacity to seamlessly handle mixed-type tabular data. For more details, see
Vu et al. (2025) <doi:10.48550/arXiv.2505.21441>.

Binh Duc Vu

RFAE

Autoencoding Random Forests

Jan Kapar

Marvin N. Wright

David S. Watson

post_x function

<dl><dt>x</dt>
<dd>Input <code>data.frame</code>.</dd>
<dt>meta</dt>
<dd>Metadata.</dd>
<dt>round</dt>
<dd>Round continuous variables to their respective maximum precision
in the real data set?</dd></dl>

Arguments

Post-process data — post_x

<dl>

<dt>x</dt>
<dd>Input <code>data.frame</code>.</dd>


<dt>meta</dt>
<dd>Metadata.</dd>


<dt>round</dt>
<dd>Round continuous variables to their respective maximum precision
in the real data set?</dd>

</dl>

post_x: Post-process data

Description

Usage

Value

Arguments