mlr3_init_predictor

Create an initial predictor function from a trained mlr3 learner

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

mlr3_init_predictor function

<dl><dt>learner</dt>
<dd><code>mlr3::Learner</code>
A trained learner used for initialization.</dd></dl>

Arguments

Create an initial predictor function from a trained mlr3 learner — mlr3_init_predictor

<dl>

<dt>learner</dt>
<dd><code>mlr3::Learner</code>
A trained learner used for initialization.</dd>

</dl>

mlr3_init_predictor: Create an initial predictor function from a trained mlr3 learner

Description

Usage

Value

Arguments

Examples