otu_mini_cont_results_nocv

<p>Results from running the pipeline with glmnet on <code>otu_mini_bin</code> with <code>Otu00001</code>
as the outcome column,
using a custom train control scheme that does not perform cross-validation</p>

datasets

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

Tuomas Borman

otu_mini_cont_results_nocv function

<p>An object of class <code>list</code> of length 4.</p>

Format

Results from running the pipeline with glmnet on otu_mini_bin with Otu00001
as the outcome column,
using a custom train control scheme that does not perform cross-validation — otu_mini_cont_results_nocv

Results from running the pipeline with glmnet on <code>otu_mini_bin</code> with <code>Otu00001</code>
as the outcome column,
using a custom train control scheme that does not perform cross-validation

otu_mini_cont_results_nocv: Results from running the pipeline with glmnet on `otu_mini_bin` with `Otu00001` as the outcome column, using a custom train control scheme that does not perform cross-validation

Description

Usage

Arguments

Format