XGBoost: Machine Learning Library for Gradient Boosting
XGBoost helps software companies implement machine learning models. It is designed for teams that need to process large datasets across distributed cloud environments.
At a glance
- Category
- Software Development
- Best for
- Software companies, Data science teams, Machine learning engineers
- Pricing
- Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- Classification Tasks, Regression Analysis, Ranking Objectives, Large-Scale Data Processing
- Integrations
- AWS, GCE, Azure, Spark, Flink
- Official website
- xgboost.ai
XGBoost is a technical library designed for gradient boosting, a machine learning framework used to solve data science problems. It is built to be portable, allowing it to run on Windows, Linux, and OS X, as well as various cloud platforms.
It is intended for software companies and data science teams that require a backend for training models. The library supports several programming languages, which may help it fit into existing development stacks.
Businesses can use it for tasks such as regression and classification. Because it supports distributed training, it can handle datasets containing billions of examples when deployed on appropriate clusters.
Buyers should confirm that they have the necessary technical expertise in languages like Python, R, or Java, as this is a library for developers rather than a standalone software application.
Key Features
Provides a framework for GBDT and GBM to handle data science problems.
Supports training across multiple machines including AWS, GCE, Azure, and Yarn clusters.
Compatible with C++, Python, R, Java, Scala, and Julia.
Supports regression, classification, ranking, and user-defined objectives.
Runs on Windows, Linux, and OS X.
Use Cases
Categorizing data into specific groups for predictive modeling.
Implementing models to predict continuous numerical values based on historical data.
Developing systems to order items based on priority or relevance criteria.
Handling datasets with billions of examples using distributed cloud environments.
Best For
Integrations
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
XGBoost is used for regression, classification, and ranking tasks within data science projects using a gradient boosting framework.
It supports C++, Python, R, Java, Scala, and Julia.
Yes, the distributed version is designed to handle problems involving billions of examples using the same code.
Source category: Software Development
Source subcategory: Machine Learning Framework
