Python package for Bayesian Machine Learning with scikit-learn API
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.
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
A modular active learning framework for Python
Las características principales de modal-python/modal son: General Machine Learning, Incremental Learning, Frameworks de Machine Learning, Machine Learning Packages.
Las alternativas de código abierto para modal-python/modal incluyen: 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. aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… amazaspshumik/sklearn-bayes — Python package for Bayesian Machine Learning with scikit-learn API. csinva/imodels — Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and…
