Developed by Yandex researchers and engineers, it is the successor of the MatrixNet algorithm that is widely used within the company for ranking tasks, forecasting and making recommendations. It is universal and can be applied across a wide range of areas and to a variety of problems.
- Accurateleads or ties competition on standard benchmarks
- Robustreduces the need for extensive hyper-parameter tuning
- Easy-to-useoffers Python interfaces integrated with scikit, as well as R and command-line interfaces
- Practicaluses categorical features directly and scalably
- Extensibleallows specifying custom loss functions
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- Read the documentation
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