Fast Retraining

In this repo we compare two of the fastest boosted decision tree libraries: XGBoost and LightGBM. We will evaluate them across datasets of several domains and different sizes.

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.

Installation and Setup

The installation instructions can be found here.

Project

In the folder experiments you can find the different experiments of the project. We developed 6 experiments with the CPU and GPU versions of the libraries.

• Airline

• BCI

• Football

• Planet Kaggle

• Fraud Detection

• HIGGS

In the folder experiment/libs there is the common code for the project.

Benchmark

In the following table there are summarized the time results (in seconds) and the ratio of the benchmarks performed in the experiments:

In the next table we summarize the performance results using the F1-Score.

The experiments were run on an Azure NV24 VM with 24 cores and 224 GB memory. The machine has 4 NVIDIA M60 GPUs. In both cases we used Ubuntu 16.04.

Contributing

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.