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[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
The main features of dswah/pygam are: General Machine Learning, Machine Learning, Machine Learning Frameworks, Machine Learning Packages, Regression and Classification, Statistical Modeling.
Projects with overlapping indexed features include: scikit-multilearn/scikit-multilearn — A scikit-learn based module for multi-label et. al. classification. larsmans/seqlearn — Sequence learning toolkit for Python. aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… christophm/rulefit — Python implementation of the rulefit algorithm. davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… danielhanchen/hyperlearn — 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
A scikit-learn based module for multi-label et. al. classification
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
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.
