@@ -20,9 +20,11 @@ LightGBM is a gradient boosting framework that uses tree based learning algorith
...
@@ -20,9 +20,11 @@ LightGBM is a gradient boosting framework that uses tree based learning algorith
- Parallel and GPU learning supported.
- Parallel and GPU learning supported.
- Capable of handling large-scale data.
- Capable of handling large-scale data.
For further details, please refer to [Features](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst). Benefits from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/Microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.
For further details, please refer to [Features](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst).
[Comparison experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the [parallel experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
Benefitting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/Microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.
[Comparison experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, [parallel experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
