High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Under Apache License 2.0
By aksnzhy

machine-learning data-science data-analysis statistics fm factorization-machines ffm

What is xLearn?

xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM), all of which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data. Many real world datasets deal with high dimensional sparse feature vectors like a recommendation system where the number of categories and users is on the order of millions. In that case, if you are the user of liblinear, libfm, and libffm, now xLearn is your another better choice.

Get Started! (English)

Get Started! (中文)


xLearn is developed by high-performance C++ code with careful design and optimizations. Our system is designed to maximize CPU and memory utilization, provide cache-aware computation, and support lock-free learning. By combining these insights, xLearn is 5x-13x faster compared to similar systems.


xLearn does not rely on any third-party library and users can just clone the code and compile it by using cmake. Also, xLearn supports very simple Python and CLI interface for data scientists, and it also offers many useful features that have been widely used in machine learning and data mining competitions, such as cross-validation, early-stop, etc.


xLearn can be used for solving large-scale machine learning problems. First, xLearn supports out-of-core training, which can handle very large data (TB) by just leveraging the disk of a PC. In addition, xLearn supports distributed training, which scales beyond billions of example across many machines by using the Parameter Server framework.

How to Contribute

xLearn has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

Note that, please post iusse and contribution in English so that everyone can get help from them.

Contributors (rank randomly)

For Enterprise Users and Call for Sponsors

If you are enterprise users and find xLearn is useful in your work, please let us know, and we are glad to add your company logo here. We also welcome you become a sponsor to make this project better.

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