agaricus.test

This data set is originally from the Mushroom data set,
 UCI Machine Learning Repository.
 This data set includes the following fields:
<ul>
<li><code>label</code>: the label for each record</li>
<li><code>data</code>: a sparse Matrix of <code>dgCMatrix</code> class, with 126 columns.</li>
</ul>

datasets

Tree based algorithms can be improved by introducing boosting frameworks.
'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.
This package offers an R interface to work with it.
It is designed to be distributed and efficient with the following advantages:
1. Faster training speed and higher efficiency.
2. Lower memory usage.
3. Better accuracy.
4. Parallel learning supported.
5. Capable of handling large-scale data.
In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions.
Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.

Yu Shi

lightgbm

Light Gradient Boosting Machine

Guolin Ke

Damien Soukhavong

James Lamb

Qi Meng

Thomas Finley

Taifeng Wang

Wei Chen

Weidong Ma

Qiwei Ye

Tie-Yan Liu

Nikita Titov

Yachen Yan

Microsoft Corporation 

Dropbox, Inc. 

Jay Loden

Dave Daeschler

Giampaolo Rodola

Alberto Ferreira

Daniel Lemire

Victor Zverovich

IBM Corporation 

David Cortes

agaricus.test function

A list containing a label vector, and a dgCMatrix object with 1611
rows and 126 variables

Format

Test part from Mushroom Data Set — agaricus.test

Test part from Mushroom Data Set

agaricus.test: Test part from Mushroom Data Set

Description

Usage

Arguments

Format

References