How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
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.
The main features of aksnzhy/xlearn are: General Machine Learning, Machine Learning, Machine Learning Frameworks, Machine Learning Packages.
Projects with overlapping indexed features include: dswah/pygam — [CONTRIBUTORS WELCOME] Generalized Additive Models in Python. larsmans/seqlearn — Sequence learning toolkit for Python. danielhanchen/hyperlearn — 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… christophm/rulefit — Python implementation of the rulefit algorithm. lensacom/sparkit-learn — PySpark + Scikit-learn = Sparkit-learn.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach
CONTRIBUTORS WELCOME Generalized Additive Models in Python
