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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.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
The main features of csinva/imodels are: Explainable AI Libraries, General Machine Learning, Machine Learning Frameworks, Machine Learning Packages, Model Interpretability.
Projects with overlapping indexed features include: 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… 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. danielhanchen/hyperlearn — 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. deepchecks/deepchecks — Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality…
