calc_mean_perf

Used by <code>calc_mean_roc()</code> and <code>calc_mean_prc()</code>.

An interface to build machine learning models for
classification and regression problems. 'mikropml' implements the ML
pipeline described by Topçuoğlu et al. (2020)
<doi:10.1128/mBio.00434-20> with reasonable default options for data
preprocessing, hyperparameter tuning, cross-validation, testing, model
evaluation, and interpretation steps. See the website
<https://www.schlosslab.org/mikropml/> for more information,
documentation, and examples.

Kelly Sovacool

mikropml

User-Friendly R Package for Supervised Machine Learning
Pipelines

Begüm Topçuoğlu

Zena Lapp

Evan Snitkin

Jenna Wiens

Patrick Schloss

Nick Lesniak

Courtney Armour

Sarah Lucas

calc_mean_perf function

<dl><dt>sensspec_dat</dt>
<dd>data frame created by concatenating results of
<code>calc_model_sensspec()</code> for multiple models.</dd>
<dt>group_var</dt>
<dd>variable to group by (e.g. specificity or recall).</dd>
<dt>sum_var</dt>
<dd>variable to summarize (e.g. sensitivity or precision).</dd></dl>

Arguments

Courtney Armour
Kelly Sovacool

Author

Generic function to calculate mean performance curves for multiple models — calc_mean_perf

<dl>

<dt>sensspec_dat</dt>
<dd>data frame created by concatenating results of
<code>calc_model_sensspec()</code> for multiple models.</dd>


<dt>group_var</dt>
<dd>variable to group by (e.g. specificity or recall).</dd>


<dt>sum_var</dt>
<dd>variable to summarize (e.g. sensitivity or precision).</dd>

</dl>

calc_mean_perf: Generic function to calculate mean performance curves for multiple models

Description

Usage

Value

Arguments

Author