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In this repo we compare two of the fastest boosted decision tree libraries: [XGBoost](https://github.com/dmlc/xgboost) and [LightGBM](https://github.com/microsoft/LightGBM). We will evaluate them across datasets of several domains and different sizes.
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On July 25, 2017, we published a blog post evaluating both libraries and discussing the benchmark results. The post is [Lessons Learned From Benchmarking Fast Machine Learning Algorithms](https://blogs.technet.microsoft.com/machinelearning/2017/07/25/lessons-learned-benchmarking-fast-machine-learning-algorithms/).
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## Installation and Setup
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The installation instructions can be found [here](./INSTALL.md).
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