Varos Glossary


What is XGBoost? 

Also known as Extreme Gradient Boosting, XGBoost is a type of specialized machine learning algorithm frequently used by engineers to optimize their machine learning models. First introduced in 2014, XGBoost is designed to be efficient and easy to use, while also being capable of ingesting and working with large data sets. Notably, engineers can apply XGBoost sans hyperparameter tuning — it doesn't require any additional configuration after being installed. 

XGBoost Python and R packages were among the library's first implementations. Today, XGBoost also supports implementations for Scala, Julia, Java, Perl, and multiple other languages. It's also directly integrated with a wide range of different tools and frameworks, including Apache Spark, and distributed as a portable open-source library on Windows, Linux and OS X. 

A Brief History of XGBoost

XGBoost first gained prominence through Kaggle, an online community for data scientists. Kaggle hosts regular competitions, including a structured data contest. How that particular competition works is relatively simple — researchers and businesses post a large data set, and participants compete to produce the best model for addressing that data set. 

Nearly everyone who used XGBoost in those competitions won, and news of the algorithm quickly spread until in 2019, it was named by InfoWorld as Technology of the Year. The library has only continued to grow more popular. To understand why, it's important to first examine how XGBoost works and what it does. 

How Does XGBoost Work?

XGBoost is what's known as a gradient-boosted decision tree algorithm, providing parallel tree boosting. It's primarily used with supervised machine learning. Let's take a step back and define each of those concepts. 

  • Gradient boosting is a machine learning technique primarily used to estimate the relationships between variables (regression) or define the categories to which a data point belongs (classification). It combines multiple weak prediction models, a technique known as ensemble learning. This is also the source of the technique's name — it 'boosts' the models by creating an ensemble. 
  • In the context of machine learning, a decision tree is a hierarchical model that generally takes the form of a series of true/false or if-then-else questions. It can be used for either data classification or regression. 
  • Gradient boosting decision trees combine multiple decision trees into an ensemble. 
  • In supervised machine learning, a machine learning model is trained using well-defined, labeled training data. 

Features and Characteristics of XGBoost

  • XGBoost offers something known as regularization, making it distinct amongst gradient boosting algorithms. Regularization essentially allows you to directly mitigate overfitting by adjusting the weights and biases of each decision tree. 
  • The algorithm also features a weighted quantile sketch algorithm. This allows it to deal with non-zero entities within its feature matrix without sacrificing computational complexity. 
  • Because XGBoost is built with a block structure, it can support parallel learning and easily scale up on multicore clusters and machines. 
  • Reduced memory usage when working with large datasets thanks to built-in cache awareness. 
  • Out-of-core computing capabilities that use disk-based structures instead of in-memory structures during computation. 

Benefits of XGBoost

The benefits of using XGBoost include: 

  • A thriving community of data scientists and engineers that actively contribute to the tool's development. 
  • Full cloud integration with support for ecosystems that include Microsoft Azure, Amazon Web Services, Yarn Clusters. 
  • Because XGBoost is open-source, it's free to use, and data scientists have full freedom to experiment with it in whatever fashion they choose. 
  • An XGBoost model will typically display high predictive performance and low processing time compared to other algorithms. 
  • As a library, XGBoost is both highly flexible and interoperable, usable with a wide range of tools, ecosystems, and platforms. 
  • Scalable, portable, and highly accurate.