resample

internal

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. (2023) <https://proceedings.mlr.press/v206/watson23a.html>.

Marvin Wright

Adversarial Random Forests

Marvin N. Wright

David S. Watson

Kristin Blesch

Jan Kapar

resample function

<dl><dt>x</dt>
<dd>A vector of one or more elements from which to choose.</dd>
<dt>...</dt>
<dd>Further arguments for sample().</dd></dl>

Arguments

Safer version of sample() — resample

<dl>

<dt>x</dt>
<dd>A vector of one or more elements from which to choose.</dd>


<dt>...</dt>
<dd>Further arguments for sample().</dd>

</dl>

Safer version of sample()

resample: Safer version of sample()

Description

Usage

Value

Arguments