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 a user of liblinear, libfm, and libffm, now xLearn is another better choice.
Features
- xLearn is developed by high-performance C++ code with careful design and optimizations
- Documentation available
- xLearn does not rely on any third-party library and users can just clone the code and compile it by using cmake
- xLearn supports very simple Python and CLI interface for data scientists
- Offers many useful features that have been widely used in machine learning and data mining competitions, such as cross-validation, early-stop, etc.
- Examples included
Categories
Machine LearningLicense
Apache License V2.0Follow xLearn
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