validate_fake

Compares classes, NA/blank proportions, and simple numeric ranges.

Generate privacy-preserving synthetic datasets that mirror structure, types, factor levels, and missingness; export bundles for 'LLM' workflows (data plus 'JSON' schema and guidance); and build fake data directly from 'SQL' database tables without reading real rows. Methods are related to approaches in Nowok, Raab and Dibben (2016) <doi:10.32614/RJ-2016-019> and the foundation-model overview by Bommasani et al. (2021) <doi:10.48550/arXiv.2108.07258>.

Zobaer Ahmed

FakeDataR

Privacy-Preserving Synthetic Data for 'LLM' Workflows

validate_fake function

<dl><dt>original</dt>
<dd>data.frame</dd>
<dt>fake</dt>
<dd>data.frame (same columns)</dd>
<dt>tol</dt>
<dd>numeric tolerance for proportion differences (default 0.15)</dd></dl>

Arguments

Validate a fake dataset against the original — validate_fake

<dl>

<dt>original</dt>
<dd>data.frame</dd>


<dt>fake</dt>
<dd>data.frame (same columns)</dd>


<dt>tol</dt>
<dd>numeric tolerance for proportion differences (default 0.15)</dd>

</dl>

validate_fake: Validate a fake dataset against the original

Description

Usage

Value

Arguments