Artificial Intelligence & Machine Learning
XGBoost
Definition
XGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It is known for its speed and performance.
Why It Matters
XGBoost is a highly optimized and powerful implementation of the gradient boosting algorithm. For many years, it has been the go-to algorithm for winning machine learning competitions on structured or tabular data.
Contextual Example
A data scientist competing on Kaggle for a problem involving tabular data (like predicting customer churn from a spreadsheet of data) would almost certainly try using XGBoost, as it is famous for its high performance on such tasks.
Common Misunderstandings
- XGBoost includes several innovations over standard gradient boosting, such as regularization and parallel processing capabilities.
- It is a powerful tool, but like other tree-based ensembles, it is not as easily interpretable as simpler models.