LightGBM is a distributed gradient boosting framework that uses tree-based learning. It is histogram-based and places continuous values into discrete bins leading to faster training and more efficient memory usage.
- Faster training speed and higher efficiency
- Lower memory usage
- Better accuracy
- Support of parallel and GPU learning
- Capable of handling large-scale data
- Ability to handle missing data
- Handles categorical features out of the box
- Provides early stopping
- Supports several programming languages including Python, R, C, C++
The package reduces the time one would spend handling categorical features and missing values. It also produces good results with the default parameters.
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