.parse_categorical_features

Internal function for setting categorical features

internal

Single unified interface for end-to-end modelling of regression,
categorical and time-to-event (survival) outcomes. Models created using
familiar are self-containing, and their use does not require additional
information such as baseline survival, feature clustering, or feature
transformation and normalisation parameters. Model performance,
calibration, risk group stratification, (permutation) variable importance,
individual conditional expectation, partial dependence, and more, are
assessed automatically as part of the evaluation process and exported in
tabular format and plotted, and may also be computed manually using export
and plot functions. Where possible, metrics and values obtained during the
evaluation process come with confidence intervals.

Alex Zwanenburg

familiar

End-to-End Automated Machine Learning and Model Evaluation

Steffen Löck

Stefan Leger

Iram Shahzadi

Asier Rabasco Meneghetti

Sebastian Starke

Technische Universität Dresden 

German Cancer Research Center (DKFZ) 

.parse_categorical_features function

<dl><dt>data</dt>
<dd>data.table with feature data</dd>
<dt>outcome_type</dt>
<dd>character, indicating the type of outcome</dd>
<dt>reference_method</dt>
<dd>character, indicating the type of method used to set
the reference level.</dd></dl>

Arguments

Internal function for setting categorical features — .parse_categorical_features

<dl>

<dt>data</dt>
<dd>data.table with feature data</dd>


<dt>outcome_type</dt>
<dd>character, indicating the type of outcome</dd>


<dt>reference_method</dt>
<dd>character, indicating the type of method used to set
the reference level.</dd>

</dl>

Internal function for setting categorical features

.parse_categorical_features: Internal function for setting categorical features

Description

Usage

Value

Arguments

Details