summary.lgb.Booster

Show summary information about a LightGBM model object (same as <code>print</code>).
New in version 4.0.0

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.

James Lamb

lightgbm

Light Gradient Boosting Machine

Yu Shi

Guolin Ke

Damien Soukhavong

Qi Meng

Thomas Finley

Taifeng Wang

Wei Chen

Weidong Ma

Qiwei Ye

Tie-Yan Liu

Nikita Titov

Yachen Yan

Microsoft Corporation 

Dropbox, Inc. 

Alberto Ferreira

Daniel Lemire

Victor Zverovich

IBM Corporation 

David Cortes

Michael Mayer

summary.lgb.Booster function

<dl><dt>object</dt>
<dd>Object of class <code>lgb.Booster</code></dd>
<dt>...</dt>
<dd>Not used</dd></dl>

Arguments

Summary method for LightGBM model — summary.lgb.Booster

<dl>

<dt>object</dt>
<dd>Object of class <code>lgb.Booster</code></dd>


<dt>...</dt>
<dd>Not used</dd>

</dl>

Summary method for LightGBM model

summary.lgb.Booster: Summary method for LightGBM model

Description

Usage

Value

Arguments