trained_rf

<p>A trained model using the default Random Forest settings</p>

datasets

A novel machine learning method for plant viruses diagnostic using
genome sequencing data. This package includes three different machine
learning models, random forest, XGBoost, and elastic net, to train and
predict mapped genome samples. Mappability profile and unreliable regions
are introduced to the algorithm, and users can build a mappability profile
from scratch with functions included in the package. Plotting mapped sample
coverage information is provided.

Xuekui Zhang

iimi

Identifying Infection with Machine Intelligence

Haochen Ning

Ian Boyes

Ibrahim Numanagić

Michael Rott

Li Xing

trained_rf function

<p>An object of class <code>randomForest.formula</code> (inherits from <code>randomForest</code>) of length 19.</p>

Format

A trained model using the default Random Forest settings — trained_rf

A trained model using the default Random Forest settings

trained_rf: A trained model using the default Random Forest settings

Description

Usage

Arguments

Format