process_cont_feats

internal

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

process_cont_feats function

<dl><dt>features</dt>
<dd>Dataframe of features for machine learning</dd>
<dt>method</dt>
<dd>Methods to preprocess the data, described in
<code><a href="/link/caret%3A%3ApreProcess()?package=mikropml&version=1.6.1" data-mini-rdoc="mikropml::caret::preProcess()">caret::preProcess()</a></code> (default: <code>c("center","scale")</code>, use <code>NULL</code> for
no normalization).</dd></dl>

Arguments

Zena Lapp, <a href="/link/zenalapp%40umich.edu?package=mikropml&version=1.6.1" data-mini-rdoc="mikropml::zenalapp@umich.edu">zenalapp@umich.edu</a>

Author

Preprocess continuous features — process_cont_feats

<dl>

<dt>features</dt>
<dd>Dataframe of features for machine learning</dd>


<dt>method</dt>
<dd>Methods to preprocess the data, described in
<code><a href='https://rdrr.io/pkg/caret/man/preProcess.html'>caret::preProcess()</a></code> (default: <code>c("center","scale")</code>, use <code>NULL</code> for
no normalization).</dd>

</dl>

Zena Lapp, <a href='mailto:zenalapp@umich.edu'>zenalapp@umich.edu</a>

process_cont_feats: Preprocess continuous features

Description

Usage

Value

Arguments

Author

Examples