AI TOOL PROFILE
XGBoost: Machine Learning Library for Gradient Boosting
- Software Development
- Machine Learning Framework
- 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.
At a glance
- Best for
- Software companies, Data science teams, Machine learning engineers
- Key use cases
- Classification Tasks, Regression Analysis, Ranking Objectives, Large-Scale Data Processing
- Integrations
- AWS, GCE, Azure, Spark, Flink
- Official website
- Visit xgboost official website
How AI is used
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
Parallel Tree Boosting
Provides a framework for GBDT and GBM to handle data science problems.
Distributed Training
Supports training across multiple machines including AWS, GCE, Azure, and Yarn clusters.
Multi-Language Support
Compatible with C++, Python, R, Java, Scala, and Julia.
Broad Task Support
Supports regression, classification, ranking, and user-defined objectives.
Cross-Platform Compatibility
Runs on Windows, Linux, and OS X.
Use Cases
Classification Tasks
Categorizing data into specific groups for predictive modeling.
Regression Analysis
Implementing models to predict continuous numerical values based on historical data.
Ranking Objectives
Developing systems to order items based on priority or relevance criteria.
Large-Scale Data Processing
Handling datasets with billions of examples using distributed cloud environments.
Integrations
- AWS
- GCE
- Azure
- Spark
- Flink
FAQ
What is XGBoost used for?
- XGBoost is used for regression, classification, and ranking tasks within data science projects using a gradient boosting framework.
Which programming languages does XGBoost support?
- It supports C++, Python, R, Java, Scala, and Julia.
Can XGBoost handle very large datasets?
- 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
More tools in Software Development
Other published listings in the Software Development category.
More tools in the Machine Learning Framework software type
Related listings that share the same software type for comparison and shortlisting.
