XGBoost and LightGBM – Summary

XGBoost and LightGBM are powerful machine learning algorithms used mainly for classification and regression problems. These algorithms very easily learn from examples and are based on decision trees. A common technique with these algorithms is called Gradient Boosting. XGBoost and LightGBM are particularly versatile and popular because they offer very high accuracy and operational speed, while performing exceptionally well with large datasets.

XGBoost and LightGBM are utilized across multiple domains, including high-stakes competitions in data science, real-world applications of artificial intelligence, finance, medicine, and recommendation algorithms. While they each employ the same fundamental concepts, they differ in terms of implementation speed, memory consumption, and the mechanisms for construction of the trees.

What is Gradient Boosting?

In Gradient Boosting, learning occurs using a stepwise approach and models are generated one step at a time. Each successive model is trained to mitigate the errors from the one previous.

Instead of constructing a single, powerful model, Gradient Boosting creates a powerful model by combining multiple weak decision trees.

What is XGBoost?

XGBoost is the abbreviation of Extreme Gradient Boosting. XGBoost is a revision and also an improvement of ordinary gradient boosting.

XGBoost takes an iterative approach to building decision trees, where each subsequent tree attempts to correct the errors of the previous one. The tool is able to automatically work with missing values, and is best suited for smaller and medium sized datasets. The combination of all of the above features leads to XGBoost being one of the most popular choices Data Science competitions due to the reliability, accuracy, and stability that competitors address.

What is LightGBM?

LightGBM is an acronym for Light Gradient Boosting Machine, and was developed by Microsoft with the primary objective of enhancing both speed and efficiency.

Key points of LightGBM

LightGBM employs a leaf-wise tree growth strategy, as opposed to level-wise tree growth. In addition to utilizing less memory, LightGBM also offers faster training time compared to XGBoost, and is able to effectively address very large datasets, making it perfect for real-time applications.

Due to the high-performance systems it is designed for, LightGBM is best suited for situations where speed is of the utmost importance.

Functions of XGBoost and LightGBM

• The algorithms work the same way. The steps are the following:
• Start with a basic model
• Assess the prediction errors
• Develop another tree to correct the errors
• Integrate all the trees to create a strong model
• This process is repeated until the errors are minimized.
• The primary distinction is in tree growth.
• XGBoost expands trees in a level-by-level manner.
• LightGBM expands trees in a leaf-by-leaf manner.

Benefits of Using XGBoost and LightGBM

• XGBoost Benefits:
• High prediction accuracy
• Overfitting prevention
• Good stability
• Good performance with structured data
• LightGBM Benefits:
• Training speeds are extreme
• Memory usage is low
• Large datasets are the best use case
• Storage for real-time systems is limited

Simplified Differences Between XGBoost and LightGBM

XGBoost is very accurate, but slow to do so, and LightGBM is fast and uses less memory

XGBoost implements level-wise growth for trees

LightGBM uses leaf-wise growth

H:2 Applications

Predicting and especially detecting:

Fraud

Stock prices

Diagnosis in the medical field

Systems for recommendations

Churning of customers

Conclusion

LightGBM and XGBoost are very powerful algorithms for boosting gradients. XGBoost zeroes in on accuracy and stability by churning slowly. LightGBM is all about speed and smooth system performance. The ultimate balancing of the two determines the size of the dataset, how fast responses are required, and the system’s available resources.