trained_xgb

<p>A trained model using the default XGBoost 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_xgb function

<p>An object of class <code>raw</code> of length 130645.</p>

Format

A trained model using the default XGBoost settings — trained_xgb

A trained model using the default XGBoost settings

trained_xgb: A trained model using the default XGBoost settings

Description

Usage

Arguments

Format