lightgbm

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.

Last 30 days

This package has been downloaded 7,033 times in the last 30 days. A solid achievement! Enough downloads to get noticed at department meetings. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 190 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 70,366 times in the last 365 days. This work is reaching a lot of screens. A significant achievement indeed! The day with the most downloads was Jul 26, 2024 with 545 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.