How LightGBM Works?
LightGBM creates a decision tree that develops leaf-wise, which implies that given a condition, just one leaf is split, depending on the benefit. Sometimes, especially with smaller datasets, leaf-wise trees might overfit. Overfitting can be prevented by limiting the tree depth. A histogram of the distribution is used by LightGBM to bucket data into bins. Instead of using every data point, the bins are used to iterate, calculate the gain, and divide the data. Additionally, a sparse dataset can benefit from this method’s optimization. Exclusive feature bundling, which refers to the algorithm’s combining of exclusive features to reduce dimensionality reduction and speed up processing, is another element of LightGBM.
There is another algorithm of lightGBM that is used for sampling the dataset i.e., GOSS (Gradient-based One Side Sampling). Data points with greater gradients are given more weight when computing gain by GOSS. Instances that have not been effectively used for training contribute more in this manner. To maintain accuracy, data points with smaller gradients are arbitrarily deleted while some are kept. Given the same sampling rate as random sampling, this approach is often superior.
Regression using LightGBM
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. Let’s see what is LightGBM and how we can perform regression using LightGBM.
Table of Content
- What is LightGBM?
- How LightGBM Works?
- Implementation of LightBGM
- Exploratory Data Analysis
- Data Preprocessing
- Regression Model using LightGBM
- Conclusion