What is LightGBM and How it Works?

The “Light Gradient Boosting Machine,” an open-source, high-performance gradient boosting system, was developed for efficient and scalable machine learning applications. Because it is created expressly for speed and accuracy, it is a well-liked alternative for both organized and unstructured data in a range of areas. A few of LightGBM’s important characteristics include support for parallel and distributed processing, the ability to manage enormous datasets with millions of rows and columns, and better gradient-boosting techniques. LightGBM is known for its superior performance and little memory usage because of histogram-based techniques and leaf-wise tree growth.

An ensemble of decision trees is used by LightGBM, a powerful gradient-boosting framework renowned for its speed and precision. It begins with data that is arranged as instances and features and initializes a fundamental model with a wrong forecast. Following that, LightGBM establishes an objective function to calculate prediction errors and modifies model predictions using gradient data from this function. It is unique in that it builds trees according to the size of a leaf, choosing splits that minimize loss and producing deep, effective trees. It uses regularization techniques and early halting to stop overfitting. The efficiency of lightGBM is derived from histogram-based approaches, memory optimization, etc. The final forecast integrates contributions from individual trees.

In this article, we will learn about one of the state-of-the-art machine learning models: Lightgbm or light gradient boosting machine. After improvising more and more on the XGB model for better performance XGBoost which is an eXtreme Gradient Boosting machine but by the lightgbm we can achieve similar or better results without much computing and train our model on an even bigger dataset in less time.

In this article, we will use this dataset to perform a classification task using the lightGBM algorithm. But to use the LightGBM model we will first have to install the lightGBM model using the below command:

Installing Packages

!pip install lightgbm