LightGBM

LightGBM is an open-source machine learning library for Microsoft Windows, Linux, Mac OS, and other operating systems. It is based on gradient boosted decision trees and is used to predict the target values of a data set by building a tree-based model utilizing binary trees. LightGBM is an efficient and high-performance implementation of the gradient boosting framework. The library has advantage of being faster than existing boosting algorithms and it is best suited for large-scale data sets.

LightGBM is built on top of the well-known C++ library known as LGBM. This library allows users to support features such as distributed learning, large datasets, and DFS. LightGBM is a distributed gradient boosting framework designed for heterogeneous computing and enables training of boosted trees on data parallelly.

LightGBM can be run in directly from the command line or integrated with existing codebases as a C++ shared or static library. LightGBM offers advantages over other boosting–machine learning libraries, including faster training, higher efficiency, more parallelism, and better accuracy. The library is optimized for sparse data, which is best for large collections of unstructured or semi-structured data, such as images, text, or audio.

LightGBM is popular in many industries, particularly in finance, e-commerce, and healthcare. It has been used to predict customer churn, analyze customer buying behavior, identify market trends, and optimize portfolio performance. It is also used in production and engineering for optimization of workflow and allocation of resources.

LightGBM is a powerful and easy-to-use machine learning library, making it a popular choice among data scientists and machine learning engineers.