GBoost
GBoost is an open-source machine learning framework based on gradient boosting. It is designed for efficiency, flexibility and extensibility. GBoost provides efficient parallel tree learning and supports various objective functions and evaluation metrics.
opensource
gradient-boosting
parallel-processing
machine-learning
GBoost: Open-Source Machine Learning Framework
Efficient, flexible, and extensible, GBoost is a popular open-source framework for gradient boosting machine learning, supporting parallel tree learning and various objective functions and evaluation metrics.
What is GBoost?
GBoost is an open-source machine learning framework for gradient boosting. It is designed to be highly efficient, flexible and extensible.
Some key features of GBoost include:
- Efficient parallel tree learning using all CPU cores during training
- Support for various objective functions including regression, classification and ranking
- Customizable evaluation metrics
- Handling missing values and weighted samples
- Asynchronous model predictions for low-latency serving
- Model interpretation tools
- Easy integration with various data sources
GBoost allows building gradient boosting models faster by leveraging parallel computing on multi-core machines. The prediction latency can be reduced significantly with asynchronous model predictions. It focuses on flexibility by supporting multiple objective functions and evaluation metrics. The model interpretation tools help users understand and debug the models.
Overall, GBoost is a good choice for teams/organizations looking for an open-source gradient boosting framework that scales well, is customizable for different use cases and easy to integrate into production.
GBoost Features
Features
- Efficient parallel tree learning
- Supports various objective functions and evaluation metrics
- Highly flexible and extensible architecture
- GPU acceleration
- Out-of-core computing
- Cache-aware access
- Asynchronous networking
Pricing
- Open Source
Pros
Very fast training speed
Low memory usage
Easy to use
Good model accuracy
Extendable and customizable
Cons
Limited documentation
Not as user-friendly as XGBoost or LightGBM
Smaller user/developer community than leading GBDT frameworks
Official Links
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