one_hot

Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
'Multi-Accuracy Boosting' (2019) <doi:10.48550/arXiv.1805.12317> for the multi-calibration of a machine learning model's prediction.
'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups.
Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are.
This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.

Sebastian Fischer

mcboost

Multi-Calibration Boosting

Florian Pfisterer

Susanne Dandl

Christoph Kern

Carolin Becker

Bernd Bischl

one_hot function

<dl><dt>labels</dt>
<dd><code><a href="/link/factor?package=mcboost&version=0.4.3" data-mini-rdoc="mcboost::factor">factor</a></code> 
Factor to encode.</dd></dl>

Arguments

One-hot encode a factor variable — one_hot

<dl>

<dt>labels</dt>
<dd><code><a href='https://rdrr.io/r/base/factor.html'>factor</a></code> 
Factor to encode.</dd>

</dl>

One-hot encode a factor variable

one_hot: One-hot encode a factor variable

Description

Usage

Value

Arguments

Examples