set_hparams_xgbTree

Set hyperparameters for SVM with radial kernel

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

set_hparams_xgbTree function

<dl><dt>n_samples</dt>
<dd>number of samples in the dataset</dd></dl>

Arguments

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

Author

Set hyperparameters for SVM with radial kernel — set_hparams_xgbTree

<dl>

<dt>n_samples</dt>
<dd>number of samples in the dataset</dd>

</dl>

Kelly Sovacool, <a href='mailto:sovacool@umich.edu'>sovacool@umich.edu</a>

set_hparams_xgbTree: Set hyperparameters for SVM with radial kernel

Description

Usage

Value

Arguments

Author

Examples