.check_feature_availability

This function checks whether feature columns can be found in the data set.
It will raise an error if any feature columns are missing from the data set.

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) 

.check_feature_availability function

<dl><dt>data</dt>
<dd>Data set as loaded using the <code>.load_data</code> function.</dd>
<dt>feature</dt>
<dd>Character string(s) indicating one or more features.</dd></dl>

Arguments

Internal function to check whether feature columns are found in the data — .check_feature_availability

<dl>

<dt>data</dt>
<dd>Data set as loaded using the <code>.load_data</code> function.</dd>


<dt>feature</dt>
<dd>Character string(s) indicating one or more features.</dd>

</dl>

Internal function to check whether feature columns are found in the data

.check_feature_availability: Internal function to check whether feature columns are found in the data

Description

Usage

Arguments