A scikit-learn based module for multi-label et. al. classification
Las características principales de scikit-multilearn/scikit-multilearn son: General Machine Learning, Machine Learning, Frameworks de Machine Learning, Machine Learning Packages, Regression and Classification.
Las alternativas de código abierto para scikit-multilearn/scikit-multilearn incluyen: dswah/pygam — [CONTRIBUTORS WELCOME] Generalized Additive Models in Python. davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… 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. danielhanchen/hyperlearn — 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. larsmans/seqlearn — Sequence learning toolkit for Python.
CONTRIBUTORS WELCOME Generalized Additive Models in Python
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.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
