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.
A modular active learning framework for Python
The main features of modal-python/modal are: General Machine Learning, Incremental Learning, Machine Learning Frameworks, Machine Learning Packages.
Projects with overlapping indexed features include: 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…
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).
